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Heliyon. 2024 May 15; 10(9): e29983.

Published online 2024 Apr 21. doi:10.1016/j.heliyon.2024.e29983

PMCID: PMC11059116

PMID: 38694023

Xu Cong^{a,}^{⁎} and Wenjia Zhang^{b}

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## Associated Data

- Data Availability Statement

## Abstract

In recent years, 3D fashion design has been relied on for improving attire fashion, design, and presentation with fewer flaws and better visualization. This aids consumers in providing visualized recommendations on modifications, suggestions, and customized attire designs. Considering the influence of automation and intelligent processing in the fashion designing industry, it introduces a Flaw Detection Method in 3D Representation (FDM-3DR) to reduce frequent modifications. The proposed method visualizes the design in three dimensions for its completeness and flawless representation. Based on the consumer recommendation, the lack of design flaws in the representation is identified, and multiple detections are presented. This is required to improve consumer satisfaction and the multi-dimensional projection between flaws and complete attire products. The learning is trained using the fixable representation, and therefore, the previous unsuitable designs are repelled by different recommendations. This improves the design adaptability, recommendation ratio, and representation ratio. Besides, it reduces the recommendation time and flaws.

**Keywords: **3D, Deep learning, Fashion design, Flaw detection, Recommendation system

## 1. Introduction

Fashion design is one of the types of art that is used to design clothes. Fashion design has various techniques and trends that can be used to improve clothing design. Both cultural and modern trends that fulfil users' interest in clothes are used in fashion design [1]. Fashion designing is the art of creating new clothes and accessories for customers. Three-dimensional (3D) technology is the most commonly used technology in the fashion design process. 3D helps the designer design sample clothing. 3D technology reduces the designing process's time and waste rate [2]. 3D provides creative ideas and perspectives to the designers that improve the efficiency of the fashion designing process. 3D is also used here to improve the accuracy rate in a decision-making process that enhances the feasibility and reliability of the system [3]. 3D-based services are also available in fashion design, such as 3D virtual and 3D designing processes. 3D virtual lets designers view different patterns and features via a virtual formatting process. 3D virtual also provides a better accuracy rate in identifying a particular product's quality during the designing process [4]. 3D technology creates a printing format that produces garments with various designs. Fashion design companies mostly use 3D printing to reduce labour and time consumption rates in producing garments [5].

Hierarchical or hierarchy is nothing by rating or ranking in an organization and system. Hierarchical perceptions are necessary for a 3D-based fashion design system. A hierarchical system identifies the exact position and raking of products produced via 3D fashion design [6]. The hierarchical system utilizes the keywords and key principles of garments. Key principles play a major role in the raking process. Hierarchical perceptions provide optimal services and advice to fashion designers that set a path to achieve certain goals [7]. 3D-based fashion design has various drawbacks that reduce the trustworthiness among the customers. A hierarchical system improves the trustworthiness of customers by increasing the accuracy rate in the identification and classification process [8]. Hierarchical principles contain social goals that provide appropriate instructions to the design during the design process. Visual-related hierarchical perception is mostly used in 3D fashion design [9]. The visual-related design provides various ideas to designers that reduce the overall time consumption rate during the designing phase. Visual design provides attractive products to customers that improve the social goal level of fashion design [10].

Deep learning (DL) is one of the types of machine learning (ML) techniques. DL utilizes human intelligence to perform a particular task in an application. DL is most commonly used for predicting and modelling [11]. Human intelligence is used here to gain a certain type of knowledge about computers and systems. DL techniques are used in 3D fashion design to improve the performance rate of the designing process [12]. The convolutional neural network (CNN) approach is mostly used in the DL technique in 3D fashion design. CNN is used here to improve the accuracy rate in the detection and decision-making process. CNN-based architecture provides a feasible set of services for fashion designs [13]. CNN's approach enhances the performance and feasibility of the fashion design process. The feature extraction method that identifies the important set of features in the designing process is used in CNN [14]. The DL-based adversarial network is also used in 3D fashion design to identify the quality of garments for customers [15]. The objective and rationale of this study are to investigate the integration of deep learning techniques into the process of creating and manipulating virtual fashion prototypes. The suggested technology may encourage designers to be more imaginative and unique by making virtual versions of fashion designs more accurate to reality. However, this study has a limitation in finding errors in complex 3D models, which could be a computationally intensive process, particularly in settings with limited resources. This could create problems for systems that identify real or near real-time defects.

The main objective and contribution of this paper is.

•

Designing the proposed FDM-3DR with a dimensional visualization recommendation system for improving consumer satisfaction and complete 3D fashion attire products.

•

The proposed method classifies the input based on augmentation and existing feature classification to improve the training rate.

•

The numerical results were performed using CLO 3D, and the proposed method achieved a high recommendation ratio, representation ratio, and efficiency ratio, reducing the recommendation time based on deep learning methods.

The upcoming section is as follows: section 2 describes the related works, section 3 examines the proposed methodology, section 4 describes the results and discussion and section 5 concludes the overall paperwork.

## 2. Related works

Liu et al. [16] proposed a new fashion sketch design for designers. The proposed sketch design first finds the management system's fashion patterns and features. Fashion designers and pattern makers use fashion sketches to design certain merchants for the customers. The proposed model mainly associates fashion sketches and patterns with human body dimensions.

Dong et al. [17] introduced an attribute-specific embedding network (ASEN) for the similarity prediction process in the fashion designing system. ASEN is used to identify the fine-grained similarities in the designing process. Region of interest (ROI) and garment patterns are also identified here, providing an optimal data set for the prediction process. The proposed ASEN method achieves a high accuracy rate in the prediction process, which enhances the system's efficiency.

Liu et al. [18] proposed a neural graph filtering-based fashion collocation framework for a fashion recommendation system. A neural graph network detects the relationship of inner garments and produces a feasible data set for the collocation framework. A style classifier is used here to find fashion styles, patterns, and features. The proposed collocation framework improves the performance and quality rate of garments.

Dong et al. [19] introduced an interactive knowledge-based design recommendation system (IKDRS). The proposed IKDRS is mainly used for the fashion designing process to provide reliable information for fashion designers. Fuzzy techniques that identify the human body's structure and key values for the IKDRS framework are used here. IKDRS increases the efficiency and effectiveness of the system. The proposed IKDRS improves the quality of designs.

Sun et al. [20] proposed a multimodal framework based on machine learning (ML) approaches. The convolutional neural network (CNN) approach is used here for the visual embedding process, which reduces the overall time consumption rate in the design process. The long short-term memory (LSTM) approach is used here for the semantic embedding process. CNN and LSTM provide visual information for customers when designing certain products. The proposed multimodal framework increases the performance and effectiveness rate of the fashion designing system.

Liu et al. [21] introduced an attribute-based generative adversarial network (GAN) model for the clothing matching system. The feature extraction process identifies both semantic and structural attributes. Attributes that are extracted play a vital role in the GAN model. The GAN model produces a certain set of rules to find the semantic attributes presented in given datasets. The proposed GAN model increases the effectiveness and reliability of the clothing match system.

Longo et al. [22] proposed a decision-aid model for mass customization (MC) in the fashion design industry. The proposed method mainly uses the MC strategy planning process to produce industry control measures. MC utilizes a family-based platform that produces the necessary services for its customers. It also provides fitting and returns policies that enhance the system's efficiency. The proposed method improves the significance and feasibility of fashion industries.

Yue et al. [23] introduced a design issue graph (DIG) based fashion style recognition method. A convolutional neural network (CNN) is used in the fashion style recognition model to identify the important aspects of customers. CNN increases the quality of styles and garments, reducing the low product quality. The proposed DIG method increases the accuracy rate in the recognition process, improving the system's performance.

Wu et al. [24] proposed a new fabric defect detection method based on unsupervised characterization. Principle component analysis (PCA) is used here to analyze the important aspects and features of fabric. PCA provides an optimal set of data for the detection and perdition process. Fabric damage, power quality, improper design, and mismatching are identified via PCA. The proposed method achieves a high accuracy rate in the detection process that enhances the efficiency rate of the fashion industry.

Kotouza et al. [25] introduced a science4fashion model for fashion designers. The proposed method is most commonly used for the decision-making and detection process. The artificial intelligence (AI) approach is used here to collect all sources and data required for the decision-making process. The k-nearest neighbour (KNN) algorithm is used here to classify the data and produce an optimal information set for the decision-making process. The proposed science4fashion model enhances the efficiency and effectiveness of the fashion designing system.

Zhang et al. [26] proposed a deep learning (DL) based framework for the clothing retrieval process in video advertising. The convolutional neural network (CNN) model finds the important features for clothing retrieval. CNN also identifies customers’ face verification, preference, and interest in clothing. The proposed framework improves the efficiency and reliability of the clothing retrieval process in a video advertising system.

Gu et al. [27] introduced a multimodal and multi-domain framework using embedding learning for the fashion analysis process. The embedding learning approach identifies both heterogeneous and hom*ogenous similarities. A multi-domain framework is also used for the fashion retrieval process to enhance the system's significance. The multimodal framework reduces data loss in the analysis process. The proposed framework increases the effectiveness and feasibility of the fashion analysis process.

Kim et al. [28] proposed a new synthesis method for the fashion image manipulation process. A classifier identifies important segments such as style and geometry. Segments provide necessary information related to images. The proposed method identifies the differences and relationships of patterns in an image. Full image synthesis is used here to find out the exact features of an image. The proposed method provides high-quality clothing segments for both customers and fashion designers.

Liu L. et al. [29] gave an exhaustive survey of activities connected to fashion analysis, including fashion identification, fashion parsing, retrieval, style learning, compatibility learning, fashionable attribute forecasting, and fashion creation. The suggested fine-classification system (F-CS) groups and organizes previous fashion learning efforts according to various tasks. The proliferation of e-commerce and fashion-sharing platforms has made massive amounts of data available to assist deep learning-based fashion approaches.

Table 1 shows the summary of the related survey. Based on the survey, there are several issues with existing methods. Hence, this study proposed the Flaw Detection Method in 3D Representation (FDM-3DR) with a dimensional visualization recommendation system for improving consumer satisfaction and complete 3D fashion attire products. The proposed method classifies the input based on augmentation and existing feature classification to improve the training rate. The numerical results of the proposed method achieved a high recommendation ratio, representation ratio, and efficiency ratio, reducing the recommendation time based on deep learning methods.

### Table 1

Summary of the related study.

Author | Method | Advantage | Limitation |
---|---|---|---|

Liu et al. [16] | New fashion sketch design | The proposed model mainly associates fashion sketches and patterns with human body dimensions. | It can take a lot of effort for a difficult fashion sketch to assimilate its pattern. |

Dong et al. [17] | Attribute-specific embedding network (ASEN) | The proposed ASEN method achieves a high accuracy rate in the prediction process, which enhances the system's efficiency. | However, if the assumption is sometimes invalid, the suggested method will fail to forecast fine-grained fashion resemblance. |

Liu et al. [18] | Neural graph filtering-based fashion collocation framework | The proposed collocation framework improves the performance and quality rate of garments. | Small size of dataset utilized. |

Dong et al. [19] | Interactive knowledge-based design recommendation system (IKDRS) | The proposed IKDRS improves the quality of designs. | Designer expectations may differ from the suggested fabric's current fashion theme level, which is determined by a collection of sensory qualities of the fabric. |

Sun et al. [20] | Multimodal framework based on machine learning (ML) | The proposed multimodal framework increases the performance and effectiveness rate of the fashion designing system. | Certain fashion products with comparable visual content tend to cluster in the hybrid feature space. |

Liu et al. [21] | Attribute-based generative adversarial network (GAN) model | The proposed GAN model increases the effectiveness and reliability of the clothing match system. | However, it's really hard to distinguish actual photos from fake ones. |

Longo et al. [22] | Decision-aid model for mass customization (MC) | The proposed method improves the significance and feasibility of fashion industries. | Nevertheless, this investigation has resulted in a greater variety of forms, higher costs, and decreased efficiency. |

Yue et al. [23] | design issue graph (DIG) based fashion style recognition method | The proposed DIG method increases the accuracy rate in the recognition process, improving the system's performance. | There is a lack of graph representation of clothing fashion. |

Wu et al. [24] | New fabric defect detection method based on unsupervised characterization | The proposed method achieves a high accuracy rate in the detection process that enhances the efficiency rate of the fashion industry. | There is a model reconstruction error. |

Kotouza et al. [25] | Science4fashion model for fashion designers | The proposed science4fashion model enhances the efficiency and effectiveness of the fashion designing system. | The execution/response system time is high. |

Zhang et al. [26] | Deep learning (DL) based framework | The proposed framework improves the efficiency and reliability of the clothing retrieval process in a video advertising system. | However, the errors are conveyed by a deep model in every module. |

Gu et al. [27] | Multimodal and multi-domain framework | The proposed framework increases the effectiveness and feasibility of the fashion analysis process. | It is not appropriate, however, for multi-domain, multi-label fashion data since class information is only available on the labels of product photos. |

Kim et al. [28] | New synthesis method | The proposed method provides high-quality clothing segments for both customers and fashion designers. | Full-image synthesis is not possible. |

Liu L. et al. [29] | An exhaustive survey | The proliferation of e-commerce and fashion-sharing platforms has made massive amounts of data available to assist deep learning-based fashion approaches. | These pixel-wise computing techniques have limitations of only using basic image attributes and strict needs for application scenarios and training data. |

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## 3. Flaw detection method in 3D representation (FDM-3DR) using deep learning

FDM-3DR is designed to improve the visualized recommendations in fashion design based on modifications, suggestions, and customized attire designs through hierarchical perception technology based on deep learning. 3D fashion design is a late adopter of hierarchical perception technology. This fashion design relies on innovation and creativity based on the industry's size and the country's socioeconomic reasons. These new technologies have taken some time to establish common practices and industry standards. However, in this fashion design, virtual reality, 3D graphics, and other digital technologies modified from electronic items and visual effects to different fashion aspects. In Fig. 1, the proposed method is portrayed.

Fig. 1

Proposed method.

Most visualization attempts in this industry reduce flaw detection at 3D representation and not at the designs where the maximum flow is determined. The industry often uses 3D fashion designs as a visualization tool for recommending, merchandising, and marketing. However, this proposed method expands on this usage to discuss how the prototyping tool and exploratory design-based 3D representation could identify unmatched design flaws in industry, education, and research. The inputs are based on designs, attire fashion, and presentation of the application of hierarchical perception technology of the 3D representation-based feature analysis performed at any instance. The main objective of this method is to reduce the recommendations and flaws. The challenging task is flaw detection and adaptability in 3D representation-based features that can be processed to identify matched and unmatched designs in 3D fashion design. The 3D representation features are stored as records with the previously identified flaw detection and adaptability analysis.

Table 2The adaptability in this industry is based on 2-dimensional (2D) and 3-dimensional (3D) visual effects, which change depending on design representation, improving the design adaptability, recommendation, and representation ratio and decreasing the unmatched designs and recommendation time. However, the attire fashion, design, and presentation based on 3D fashion do not easily adapt to the actual 3D representation. Therefore the replacement or filling of 3D design visualized recommendation is classified using deep learning based on adaptability. The lacking design flaws in the 3D representation based on the consumer recommendation are identified, and the multiple detections are identified. In this industry, 3D fashion design presentations are created based on consumer recommendations and attire fashion for better visualization through deep learning. The learning process accurately predicts consumer recommendations by creating a 3D representation in fashion designs. The flaw is identified during 3D representation based on feature-matching instances. It can be rectified through filling/replacing design recommendations based on similar features in 3D fashion design at different time intervals. The design adaptability requires different visualized recommendations and representations for improving consumer satisfaction and multi-dimensional projection between the flaws and complete attire products through design recommendation. The deep learning paradigm is used to identify the design adaptability based on dimensional variations in 3D fashion design representation through performing similarity analysis at the time of flaw detection. Therefore, if any unmatched design features are identified, that is said to be flaw detection. Then the identified flaw can be rectified through design recommendations. This continuous process fulfils the consumer demands, attire, fashion, design, presentation, and needs through 3D representation in fashion design, which is a prominent consideration. The proposed method focuses on the influence of automation and intelligent processing in the fashion designing industry, and this consideration is trained by the attire products and flaws based on design adaptability through available flawless representation using hierarchical perception technology.

### Table 2

Representation ratio analysis.

Designs | MMMDRF | IKRS | JFSRM | FDM-3DR | Detection Rate | MMMDRF | IKRS | JFSRM | FDM-3DR |
---|---|---|---|---|---|---|---|---|---|

Representation Ratio | Representation Ratio | ||||||||

10 | 42.29 | 49.49 | 61.86 | 73.524 | 0.1 | 41.89 | 45.52 | 60.14 | 77.663 |

20 | 48.06 | 54.11 | 64.09 | 73.383 | 0.2 | 41.98 | 55.387 | 70.5 | 75.673 |

30 | 45.71 | 64.75 | 71.05 | 82.185 | 0.3 | 47.48 | 54.66 | 76.55 | 80.593 |

40 | 42.93 | 56.84 | 76.19 | 88.415 | 0.4 | 51.54 | 58.12 | 72.41 | 85.478 |

50 | 47.03 | 60.77 | 71.61 | 89.986 | 0.5 | 45.01 | 49.46 | 64.8 | 89.261 |

60 | 50.32 | 64.18 | 61.81 | 94.01 | 0.6 | 46.07 | 58.6 | 78.93 | 81.317 |

70 | 49.65 | 52.59 | 76.9 | 86.484 | 0.7 | 49.06 | 60.83 | 70.1 | 82.299 |

80 | 44.34 | 58.77 | 73.5 | 81.946 | 0.8 | 52.64 | 67.42 | 77.62 | 91.574 |

90 | 51.27 | 65.93 | 79.21 | 85.55 | 0.9 | 55.32 | 58.66 | 72.62 | 87.296 |

100 | 51.87 | 62.73 | 81.79 | 89.333 | 1 | 56.34 | 71.35 | 83.27 | 94.29 |

110 | 52.63 | 68.48 | 74.91 | 92.906 | |||||

120 | 55.08 | 69.49 | 82.34 | 93.324 |

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This fashion design is based on 3D representation, and its completeness is observed through virtualized recommendations between flaws and complete attire fashion products. The flawless representation gives completeness to the 3D fashion design. In this industry, the 3D representation of fashion design considers virtualized recommendations for fashion design for time and day. Design adaptability reduces the chance of flaw detection in the fashion design industry through similarity analysis. The previous unsuitable designs are repelled through different recommendations as an instance of reducing frequent modifications in designing. The proposed method focuses on unsuitable design detection through flaw detection and deep learning recommendations. Initial fashion designing Method of inputs let $F\left(D\right)$ represent the instance of 3D fashion design observation at different time intervals based on the consumer providing visualized recommendation such that the 3D representation of design $R\left(D\right)$ is computed as

$$R\left(D\right)=F\left(D\right)+\left({f}_{d}\times {\alpha}_{d}\right)$$

(1a)

Such that,

$$\mathit{arg}\underset{i}{\mathrm{min}}\sum \left[{f}_{d}\left(t\right)+{\alpha}_{d}\left(t\right)\right]\phantom{\rule{0ex}{0ex}}\forall \phantom{\rule{0ex}{0ex}}F\left(D\right)$$

(1b)

In the above equations (1a), (1b), the variable ${f}_{d}$ and ${\alpha}_{d}$ is used to represent the flaw detection and design adaptability analysis in this industry and the objective of reducing flaws in all $F\left(D\right)\in R\left(D\right)$ is 3D representation. The available visualized recommendations based on consumer needs provide suggestions, modifications, customized attire designs, and different recommendations are observed from the instances. The continuous visualized recommendation $\left({V}_{r}\right)$ relies on 3D fashion designing and its completeness $\left(C\right)$ based on flawless representation ${f}_{L}R$ is computed in the following equation. Therefore, if the condition ${f}_{L}R={V}_{r}+C$ is performed such that $C$ is identified between the ${V}_{r}$ flaws and complete attire products. If ${n}_{r}$ denotes the number of 3D representations, and $C=\left({n}_{r}\times {f}_{L}R\right)-{V}_{r}$ the discrete 3D design at different intervals will be rectified based on matched features. Let $\rho \left({V}_{r}\right)$ and $\rho \left(C\right)$ is used to denote the 3D representation based on $F\left(D\right)$ processed in ${n}_{r}$ instances and ${f}_{d}$ is identified in all virtualized recommendations such that

$$\rho \left({V}_{r}\right)={n}_{r}{V}_{r}\times F\left(D\right);\therefore {f}_{d}=0$$

(2)

Where,

$$\rho \left(C\right)=\frac{{f}_{d}}{{n}_{r}}{V}_{r}\times F\left(D\right);\therefore {f}_{d}\ne 0$$

(3)

From equations (2), (3), the fashion design-based 3D representation features are observed in $\left({n}_{r}\times {f}_{L}R\right)$ and $\left(\frac{{f}_{d}}{{n}_{r}}\times C\right)$ instances are analyzed with $F\left(D\right)$. The representation based on the above considerations is illustrated in Fig. 2.

Fig. 2

Representation based on $\mathit{\rho}\left({\mathit{V}}_{\mathit{r}}\right)$**and**$\mathit{\rho}\left(\mathit{C}\right)$.

The product representation is required to identify flaws and completeness in the available designs. Depending on ${f}_{d}$ and ${\alpha}_{d}$ the recommendations for $\rho \left({V}_{r}\right)$ and $\rho \left(c\right)$ is performed. This relies on the completeness observed (as recommended) by the user in the previous representation (Refer to Fig. 2). Here, based on the 3D representation, feature analysis as in equations (2), (3) is re-written as

$$R\left(D\right)=\{\begin{array}{c}\rho \left({V}_{r}\right)={n}_{r}{V}_{r}:\to F\left(D\right),\therefore {f}_{d}=0\\ \rho \left({V}_{r}\right)-\rho \left(C\right)={n}_{r}{V}_{r}:\to F\left(D\right)-\frac{{f}_{d}}{{n}_{r}}{V}_{r}\to {f}_{d}\times F\left(D\right),\therefore {f}_{d}\ne 0\end{array}$$

(4)

From the above equation (4), expanded 3D fashion design representation, the analysis of $C\in {f}_{L}R$ is to be estimated previously on modifying the first instance of $C$ providing visualized recommendations as in equation (5). This assessment aims to identify flaw detection in fashion design based on different design recommendations using a deep learning paradigm. The design recommendation is classified as filling/replacing features based on consumer recommendation with the available design adaptability identified through the learning process. From this analysis, the design adaptability instance based on 3D representation completeness of ${n}_{r}\in C$ is given as

$${n}_{r}\left(C\right)=\left(1-\frac{{V}_{r}}{{n}_{r}}\right){t}_{n-1}+\sum _{i=1}{\left(1-\frac{{f}_{d}}{{n}_{r}}\right)}^{{U}_{d-1}}$$

(5)

Equation (5) estimates the instance of previous unsuitable designs ${U}_{d-1}$ (i.e.) the observation of current features is matched with the previous 3D representation features is the precise virtualized recommendation for fashion designs and therefore, ${n}_{r}\left(C\right)=\left(1-\frac{{V}_{r}}{{n}_{r}}\right){t}_{n-1}+\sum _{i=1}{\left(1-\frac{{f}_{d}}{{n}_{r}}\right)}^{{U}_{d-1}}$ is estimated for continuous design recommendations. Hence, based on the design adaptability, $R\left(D\right)=\rho \left({V}_{r}\right)-\rho \left(C\right)\left[1-{n}_{r}\left(C\right)\right]$ is the final output for completeness, therefore ${f}_{d}\ne 0$ in this 3D representation. The flaw rectification is performed based on consumer recommendations $\left({\exists}_{{V}_{r}}\right)$ and $\left({\exists}_{C}\right)$ for design adaptability analysis at the initial method is computed as

$${\exists}_{{V}_{r}}\simeq \left[\frac{\rho \left({V}_{r}\right).{L}_{df}}{\sum _{i\in 1}{\left[{n}_{r}\phantom{\rule{0ex}{0ex}}{V}_{r}\phantom{\rule{0ex}{0ex}}F\left(D\right)\right]}_{i}}\right]$$

(6)

Such that,

$${\exists}_{C}\simeq \left[\frac{{L}_{df}\left(\rho \left(C\right)+\rho \left({V}_{r}\right)\right)}{\sum _{i\in 1}{\left({n}_{r}\phantom{\rule{0ex}{0ex}}{V}_{r}\right)}_{i}{\left\{\phantom{\rule{0ex}{0ex}}\left[1-{n}_{r}\phantom{\rule{0ex}{0ex}}\left({f}_{d}\right)\right]\times \rho \left(C\right)\right\}}_{i}}\right]$$

(7)

Equations (6), (7) follow the flaw rectification in the 3D representation in fashion designing observation. It is based on ${V}_{r}$ and $C$ for the instance of lacking design flaws in the representation ${\phantom{\rule{0ex}{0ex}}L}_{df}$ stored in the previous unsuitable designs. In this initial method of 3D representation, the computation of ${\exists}_{{V}_{r}}$, ${\exists}_{C}$, $\rho \left({V}_{r}\right)$ and $\rho \left(C\right)$ are the serving input for the 3D fashion design representation using a deep learning process. The flaw detection for consumer recommendations is illustrated in Fig. 3.

Fig. 3

Flaw detection for consumer recommendation.

The $\rho \left({V}_{r}\right)$ and $\rho \left(C\right)$ inputs are verified for $R\left(D\right)$ across different ${\alpha}_{d}$ features for improving the consumer recommendation probability. This is performed using the filling and design augmentation such that ${n}_{r}\left(C\right)$ and ${V}_{r}$ are classified. Depending on this consideration, the recommendation (new design or visualization) is performed. Therefore, the flaws in the ${\exists}_{{V}_{r}}$ are identified as improving the recommendations (Fig. 3). The instance of providing virtualized recommendations helps to detect the unmatched design features (flaws) in fashion design based on ${V}_{r}$ and $C\in {f}_{L}R$. This deep learning paradigm is discussed in the following session. In Fig. 4, the self-analysis for ${\exists}_{{V}_{r}}$ and ${\exists}_{C}$ is presented.

Fig. 4

Self-analysis of ${\exists}_{{\mathit{V}}_{\mathit{r}}}$**and**${\exists}_{\mathit{C}}$.

The proposed method maximizes ${n}_{r}\left(c\right)$ using flaws and recommendations. The recommendations for ${\exists}_{c}$ and ${V}_{r}$ are jointly used for mitigating errors such that ${M}_{DP}$ is mapped with $t\phantom{\rule{0ex}{0ex}}\forall \phantom{\rule{0ex}{0ex}}k$. If the matching is unsuccessful, filling or new augmentation (Design) is performed. The learning is pursued for now $\rho \left({V}_{r}\right)$ and $\rho \left(C\right)$ such that ${M}_{DP}$ is validated against $t\phantom{\rule{0ex}{0ex}}\forall \phantom{\rule{0ex}{0ex}}k$. This classifies ${f}_{d}$ and ${n}_{r}\left(c\right)$ for improving the ${\exists}_{c}$. Contrarily, the ${\exists}_{{V}_{r}}$ recognition increases the e $\rho \left(c\right)\forall R\left(D\right)$ and hence ${n}_{r}\left(C\right)$ is achieved. Based on the ${\alpha}_{d}\forall \phantom{\rule{0ex}{0ex}}F\left(D\right)$ further flaw detection is pursued. Therefore the remaining (including augmented) designs are used for ${\alpha}_{d}$ verification. This abruptly reduces flaws and is converted into user recommendations. Therefore, ${V}_{r}$ and ${\exists}_{c}$ increases the ${n}_{r}\left(C\right)$, adding up with the previous case and reducing flaws.

**Deep Learning Paradigm for Virtualized Recommendation:** In virtualized recommendations, deep learning is used to analyze the frequent modifications in ${\exists}_{{V}_{r}}$ and ${\exists}_{C}$ for performing ${f}_{L}R$. This deep learning paradigm depends on previous unsuitable designs of the 3D $F\left(D\right)$, the flexible representation output is achievable. The modifications, suggestions, and attire designs may differ for each fashion design in this industry. The stored design adaptability helps to classify design recommendations in both instances of ${n}_{r}\phantom{\rule{0ex}{0ex}}\left(C\right)$ in all ${f}_{L}R$. This learning performs two process types in this proposed method: flaw detection and design adaptability verification. In this verification based on different recommendations, the two processes are verified to improve the unsuitable designs in 3D $F\left(D\right)$. Instead, the design recommendation between the flaws and complete attire products detected in $F\left(D\right)$ is to improve the $R\left(D\right)$ along with better design representation and detection of flaws in fashion design.

The design recommendation is analyzed for 3D representation based on consumer satisfaction in $F\left(D\right)$ and ${L}_{df}$. The computation of $F\left(D\right)\in {L}_{df}$ is detected under ${V}_{r}$ and $C$ depends on the occurrence of multi-dimension projection. In this design, adaptability $F\left(D\right)$ and the recommended time is estimated independently through the deep learning process. The multi-dimension projection is computed based on ${n}_{r}\phantom{\rule{0ex}{0ex}}\left(C\right)$ and $\left({n}_{r}{L}_{df}\right)$ throughout, after which different recommendations in 3D representation are used to repel the initial consumer modifications, satisfactions, and attire designs. The multi-dimension projection output instance depends on $\left({{M}_{DP}}_{1}to\phantom{\rule{0ex}{0ex}}{{M}_{DP}}_{k}\right)$ is computed as in equation (8)

$$\begin{array}{c}{{M}_{DP}}_{1}={{V}_{r}}_{1}\\ {{M}_{DP}}_{2}=2{{V}_{r}}_{2}+2{\left(C\right)}_{2}-{f}_{d}\\ \begin{array}{c}{{M}_{DP}}_{3}=3{{V}_{r}}_{3}+3{\left(C\right)}_{3}-{f}_{d}\\ \vdots \\ {{M}_{DP}}_{k}={n}_{r}{{V}_{r}}_{k}-{n}_{r}{\left(C\right)}_{k}-{f}_{d}\end{array}\end{array}\}$$

(8)

Instead, the design adaptability is based on $\rho \left({V}_{r}\right)$ and ${L}_{df}$ in the 3D representation features in available fashion designs for training the learning process in all $k$ instances presented under $C$. The design adaptability of the fashion designs is satisfied in its first training set, in which the attire designs and modifications are analyzed alone. After finishing the matching process, the design adaptability of the design recommendation features is compared with ${L}_{df}$ based on $\rho \left(C\right)$ in the processing instance. Therefore, the learning is trained based on the instance $\left({t}_{1}\phantom{\rule{0ex}{0ex}}to\phantom{\rule{0ex}{0ex}}{t}_{k}\right)$ flexible representation is computed as

$$\begin{array}{c}{t}_{1}={{M}_{DP}}_{1}\\ {t}_{2}=2\left({M}_{DP}\right)+\rho {\left({U}_{d}\right)}_{1}\\ \begin{array}{c}{t}_{3}=3\left({M}_{DP}\right)+\rho {\left({U}_{d}\right)}_{2}-{f}_{d-1}\\ \vdots \\ {t}_{k}={n}_{r}\left({M}_{DP}\right)+\rho {\left({U}_{d}\right)}_{k-1}-{f}_{d-2}\end{array}\end{array}\}$$

(9)

In equation (9), the performance of design adaptability generates outputs based on a multi-dimension projection of fashion designs in both instances of ${V}_{r}$ and $C$ from ${{M}_{DP}}_{1}$ to ${{M}_{DP}}_{k}$ sequences and the consumer providing a visualized recommendation of available design adaptability is analyzed with $\left({t}_{1}\phantom{\rule{0ex}{0ex}}to\phantom{\rule{0ex}{0ex}}{t}_{k}\right)$ training instance. Here, the 3D recommendation system based on consumer satisfaction is verified using a flaw detection occurring instance. The condition of ${L}_{df}\in {M}_{DP}$ does not equal to ${L}_{df}\in t$ is the 3D representation features matching instance. If a virtualized recommendation is observed in the first instance, then training is provided for the learning process and computed based on completeness. This means the flawless representation is classified as per the norms of ${V}_{r}$ and ${f}_{d}$ occurrence is identified and ${n}_{r}\left({M}_{DP}\right)+\rho {\left({U}_{d}\right)}_{k-1}-{f}_{d-2}$ is the available fashion design adaptability instance based on the different recommendations. The multi-dimension projection for virtualized recommendation using deep learning is illustrated in Fig. 5.

Fig. 5

Virtualized Recommendation using Deep Learning.

The ${n}_{r}$ serves as the input for the deep learning process, validating it's ${\alpha}_{d}\phantom{\rule{0ex}{0ex}}\forall \phantom{\rule{0ex}{0ex}}C$ across $R\left(D\right)$. If $R\left(D\right)$ is feasible, then multi-dimension representation is provided along with $t$. The individual $t$ outputs are used for ${f}_{d}$ analysis provided recommendations is segregated. The distinct ${M}_{DP}\phantom{\rule{0ex}{0ex}}\forall {\alpha}_{d}=1$ is used for improving virtualized recommendations in product design. The ${{M}_{DP}}_{k}$ is used to perform the design adaptability for identifying the multiple flaw detection from the instance of virtualized recommendation as in the above equation, whereas the ${M}_{DP}$ in equation (8) is identified $C$ and the 3D representation dimensions based on equations (6), (7) are computed for its occurrence of flexible representation. Here, the 3D representation of designs $R\left(D\right)$ with the flawless representation identified from ${V}_{r}$ in ${n}_{r}\left[{{M}_{DP}}_{k}\phantom{\rule{0ex}{0ex}}{{V}_{r}}_{k}F\left(D\right)+{\exists}_{{V}_{r}}\phantom{\rule{0ex}{0ex}}\rho \left(C\right)-{f}_{d}\right]$ is the final detection of $F\left(D\right)$. This proposed method increases design adaptability and recommendation and representation ratio and reduces the recommendation time and flaws in the 3D fashion design representation. Fig. 6 presents the self-analysis of $R\left(D\right)$ and ${\alpha}_{d}$ for the varying iterations.

Fig. 6

Self-analysis $\mathit{R}\left(\mathit{D}\right)$**and**${\mathit{\alpha}}_{\mathit{d}}$**for varying**$\mathit{t}$.

For the varying $t$, the $R\left(D\right)$ and ${\alpha}_{d}$ are analyzed for different designs as presented in Table 3. The learning iterations are performed for ${M}_{DP}$ and $t\in k$ such that ${\exists}_{C}$ is increased. Depending on the ${f}_{d}\phantom{\rule{0ex}{0ex}}\forall \phantom{\rule{0ex}{0ex}}\rho \left({V}_{r}\right)$ and $\rho \left(C\right)$ are independently mapped for representation and training. Using the flaws rectification, the $F\left(D\right)$ is analyzed such that ${n}_{r}\left(C\right)$ is achieved in any $t$ or ${M}_{DP}$ to $t$ where $k$ is the same. The recommendations for $R\left(D\right)$ are based on $\rho \left({V}_{r}\right)$ and $\rho \left(C\right)$ such that ${t}_{1}$ to ${t}_{k}$ is performed. These two processes are used to maximize recommendations without leveraging the errors. In both cases, ${\alpha}_{d}$ resisting ${f}_{d}$ and flaws are identified. Such identifications are used for training new instances $\forall \phantom{\rule{0ex}{0ex}}C$ provided ${\exists}_{{V}_{r}}$ is performed in the consecutive assessment. Therefore the ${\alpha}_{d}$ is improved across different $F\left(D\right)\text{,}$ preventing new ${f}_{d}$.

### Table 3

Flaws analysis.

Designs | MMMDRF | IKRS | JFSRM | FDM-3DR | Detection Rate | MMMDRF | IKRS | JFSRM | FDM-3DR |
---|---|---|---|---|---|---|---|---|---|

Flaws (%) | Flaws (%) | ||||||||

10 | 10.91 | 8.42 | 4.49 | 1.396 | 0.1 | 22.08 | 17.74 | 12.01 | 6.086 |

20 | 12.25 | 10.67 | 5.3 | 3.352 | 0.2 | 20.54 | 16.14 | 10.42 | 5.493 |

30 | 15.66 | 9.38 | 6.36 | 2.615 | 0.3 | 21.84 | 17.19 | 9.74 | 5.222 |

40 | 18.89 | 14.03 | 10.93 | 4.313 | 0.4 | 18.61 | 15.94 | 11.65 | 5.643 |

50 | 13.32 | 11.56 | 8.54 | 2.313 | 0.5 | 18.03 | 16.69 | 5.08 | 1.424 |

60 | 16.93 | 15.53 | 10.74 | 1.951 | 0.6 | 16.03 | 13.79 | 9.43 | 5.399 |

70 | 18.66 | 12.08 | 9.14 | 5.895 | 0.7 | 19.33 | 73.89 | 5.94 | 2.783 |

80 | 15.74 | 11.37 | 8.66 | 5.677 | 0.8 | 19.78 | 11.93 | 7.02 | 2.773 |

90 | 18.14 | 15.49 | 13.03 | 4.219 | 0.9 | 18.3 | 12.82 | 7.43 | 5.59 |

100 | 19.98 | 17.56 | 12.8 | 3.537 | 1 | 13.86 | 9.01 | 4.26 | 1.393 |

110 | 20.58 | 16.05 | 10.42 | 5.975 | |||||

120 | 22.09 | 17.32 | 12.16 | 6.306 |

Open in a separate window

One aspect of 3D fashion design is flaw identification, which involves finding and fixing mistakes in digital clothes and designs before they are made. Before manufacturing begins, designers may check that their virtual clothes are up to par by having the software detect imperfections like uneven seams, fabric distortions, and fit difficulties. The ability to train defect detection algorithms to recognize imperfections unique to each person's body type or personal style opens the door to more customized designs and form-fitting clothing. Using deep learning algorithms trained on massive datasets of perfect and imperfect designs allows for implementing fault identification in 3D fashion design. Next, these algorithms may scan digital clothing for typical flaws and give designers constructive criticism. The ability to train defect detection algorithms to recognize imperfections unique to each person's body type or personal style opens the door to more customized designs and form-fitting clothing. Businesses may improve production efficiency and reduce waste by fixing problems with virtual designs before they go into production.

## 4. Discussion

This paper presents an approach to fault identification in 3D fashion design representation to optimise adaptability and the recommendation ratio. The suggested strategy sorts the input according to preexisting feature categorization and further augments to increase the training rate. In validations for categorization and defect conditions, dimensional visualization is advised when dealing with several enhanced inputs. To maximize error detection, deep learning relies on differentiating between completeness and defects for various representations. The frequent adjustments to 3D fashion design are analyzed using deep learning with virtualized suggestions. This deep learning paradigm relies on prior inappropriate 3D designs to provide flexible representation output. Variations, recommendations, and clothing designs may vary with every fashion design in this field. Based on customer satisfaction with clothing designs, the design suggestion is evaluated for a three-dimensional rendering.

Dataset Description: The biggest visual fashion research database, DeepFashion, has more than 800,000 different fashion photographs, including both staged store shots and unrestricted consumer photos. Additionally, DeepFashion has extensive information on garments that have been annotated. Fifty categories, one thousand descriptive features, a bounding box, and clothing landmarks are all identified in each picture in this collection. As a third point, DeepFashion includes more than 300,000 picture pairings that span different domains and poses. Attribute Prediction, Consumer-to-shop Clothes Retrieval, In-shop Clothes Retrieval, and Landmark Detection are the four benchmarks produced utilizing the DeepFashion database. Computer vision tasks like clothes detection, clothes recognition, and image retrieval may also use the data and annotations from these benchmarks as training and test sets.

CLO 3D is software for 3D fashion design that allows users to make virtual clothing mockups. A wide range of clothing items, such as tops, coats, caps, purses, wallets, undergarments, and swimwear, may be created with it. The source provides fashion accessories for men and women under 50 categories and 1000 features. The representation contains 1000 $\times $ 300000 pairs of projections for different accessories. Based on this information, the design of the variable (range: 10 to 120) and detection rate (range 0.1–1) are used to validate ${\alpha}_{d}$ recommendation and representation ratio, recommendation time, and flaws. In the comparative analysis, the methods (IKRS) [20] and the Joint Fashion Style Recognition method (JFSRM) [24] are used. Before the comparative analysis, the case discussion for the input from Ref. [30] is presented. The possible representations for a fashion accessory are presented in Fig. 7.

Fig. 7

Possible representation.

The representation in values the model portrays using the design accessory for ${V}_{r}$. This provides connecting features for the consumer when ordering or recommending the same. Depending on the reviews and ratings, the modifications in ${V}_{r}$ and ${\exists}_{{V}_{r}}$ mitigating (in the existing design) is recommended. It is to be noted that not all the products have a unanimous representation. Therefore ${f}_{d}$ is common in some products for which the deep learning over ${M}_{DP}$ under ${t}_{1}$ to ${t}_{k}$ is required. This representation is provided from distinct products (of the same category) for $\rho \left({V}_{r}\right)$ and $\rho \left(c\right)$ improvements. This is performed as long as $\mathrm{max}\left\{{\alpha}_{d}\right\}\phantom{\rule{0ex}{0ex}}\forall \phantom{\rule{0ex}{0ex}}{t}_{1}\phantom{\rule{0ex}{0ex}}to\phantom{\rule{0ex}{0ex}}{t}_{k}$ is observed, preventing flaws. In ${n}_{r}\left(C\right)$ detection, user recommendations, and training sequences are required. Based on the 50 categories of products from the data source, the discussion on recommendation and completeness is presented in Fig. 8. This is observed based on user reviews and ratings.

Fig. 8

Recommendation and completeness for categories.

The ${n}_{r}\left(c\right)$ and recommendations are analyzed for the different categories. This purely relies on the review and recommendation presented by the user in the previous purchase category. The ${M}_{DP}\phantom{\rule{0ex}{0ex}}\forall \phantom{\rule{0ex}{0ex}}{t}_{1}\phantom{\rule{0ex}{0ex}}to\phantom{\rule{0ex}{0ex}}{t}_{k}\phantom{\rule{0ex}{0ex}}\forall \phantom{\rule{0ex}{0ex}}{\exists}_{{V}_{r}}$ is used for validating $\rho \left({V}_{r}\right)$ and $\rho \left(C\right)$ improving the ${n}_{r}\left(C\right)$. The variations are used in the consecutive $R\left(D\right)$ such that learning aims at improving ${\exists}_{i}$.

### 4.1. Adaptability

The ${\alpha}_{d}$ is compared for the varying designs and detection ratio in Fig. 9 for the considered methods. Fig. 9 shows the x-axis as designs denote the number of virtual 3D fashion attire designs. The proposed method improves $R\left(D\right)$ based on ${\alpha}_{d}\phantom{\rule{0ex}{0ex}}\forall \phantom{\rule{0ex}{0ex}}t$ such that the $\rho \left({V}_{r}\right)$ and $\rho \left(C\right)$ are distinguishable. The $argmi{n}_{i}\phantom{\rule{0ex}{0ex}}{f}_{d}\left(t\right)\phantom{\rule{0ex}{0ex}}\forall \phantom{\rule{0ex}{0ex}}F\left(D\right)$ improves ${\alpha}_{d}$ depending on ${n}_{r}\left(c\right)$ such that ${\exists}_{{V}_{r}}$ is high. This is achieved in two ways, namely ${M}_{DP}$ analysis and ${t}_{k}$ initialization. In the consecutive $\rho \left({V}_{r}\right)$ and $\rho \left(C\right)$ for the ${\exists}_{c}$, the flaw detection is performed. The proposed method analyses the verification for ${f}_{d}$ preventing ${n}_{r}\phantom{\rule{0ex}{0ex}}\forall $ recommendation outputs. In the distinguishable representation, the ${V}_{r}$ is improved for ${\exists}_{c}$ for improving adaptability. The training instances improve the existence of ${f}_{d}=0$ using ${n}_{r}\in C$ classifications. For the varying designs (including augmented) the new ${\exists}_{c}$ is required such that it is greater than ${n}_{r}\left(c\right)$ provided $\mathrm{max}({{\alpha}_{d}\}}_{i}\phantom{\rule{0ex}{0ex}}\forall \phantom{\rule{0ex}{0ex}}i\in \left[1,k\right]$ is achievable. Depending on the varying intervals, the design verification is performed to prevent flaws in multi-dimensional representation. Therefore, new dimension filling and design augmentation features are included to maximize adaptability. This is recurrently trained for the varying $F\left(D\right)$ and detection rates improvising the ${\alpha}_{d}$.

Fig. 9

Adaptability analysis.

### 4.2. Recommendation ratio

The comparative analysis of the recommendation ratio is presented in Fig. 10. The proposed method is keen on leveraging the recommendation based on ${\alpha}_{d}$ and ${\exists}_{c}$. The joint process is split into distinct classes to preventing ${n}_{r}\left(C\right)$ failing detections. Therefore the recommendations for ${V}_{r}$ and ${\exists}_{c}$ are independently analyzed. In the first recommendation process, $\rho \left({V}_{r}\right)$ and $\rho \left(C\right)$ is used for improving ${n}_{r}\left(C\right)$. This requires less data augmentation from the new designs such that learning is performed $\forall \phantom{\rule{0ex}{0ex}}t=1\phantom{\rule{0ex}{0ex}}to\phantom{\rule{0ex}{0ex}}k$. Contrarily, for the ${\exists}_{c}$ the deciding factors are ${\exists}_{{V}_{r}}$ and $R\left(D\right)$. The multi-dimension representation categorises flaws and outcomes under filling and replacement. Post the analysis, the ${\alpha}_{d}$ maximization is performed to prevent additional $k$. This is recurrent for the maximum $F\left(D\right)$ inputs provided the $\rho \left({V}_{r}\right)$ is high. Therefore, the consumer recommendations are augmented across varying recommendations preventing new training instances. This is consistent with different recommendations for new detections and designs.

Fig. 10

Recommendation ratio analysis.

### 4.3. Representation ratio

The proposed method achieves a higher representation ratio than the others, as in for the varying designs and detection rates. The representations are based on product count and $\rho \left(C\right)$. In the first representation, the ${V}_{r}$ is required such that ${n}_{r}\left(C\right)$ is high without additional flaws. Contrarily the modifications are instigated post ${\exists}_{C}$ such that ${t}_{1}$ to ${t}_{k}$ becomes mandatory. The learning process instigates the maximum possible ${M}_{DP}$ combinations for differentiating recommendations from ${f}_{d}$. The $R\left(D\right)$ is analyzed for two cases, such as ${f}_{d}=0$ and ${f}_{d}\ne 0$; for the first case, filling is recommended to identify design completeness. The latter condition relies on the $\rho \left(c\right)$ mitigating the flaws. If there are flaws present, then the $\rho \left(C\right)>P\left({V}_{r}\right)$ is validated for further verification, improving both ${V}_{r}$ and ${\exists}_{c}$. This is based on ${\alpha}_{d}\phantom{\rule{0ex}{0ex}}\forall {n}_{r}$ provided $R\left(D\right)=1$ or $0$; contrarily for the next analysis of ${f}_{d}\ne 0$, ${M}_{DP}$ and $t$ inclusions from $1$ to $k$ are presented. This is required for maximizing the representation ratio for varying $F\left(D\right)$ and detection rates.

### 4.4. Recommendation time

The proposed method achieves less recommendation time for the varying designs and detection rates (Refer to Fig. 11). The time requirement is divided between the ${V}_{r}$ and ${\exists}_{C}$ for the consecutive analysis. In the first sequence analysis ${f}_{d}$ and ${\alpha}_{d}$ combinations are considered. The outputs are extracted from ${V}_{r}$ such that ${n}_{r}\left(C\right)$ ensures new recommendations. This requires less computation time preventing multiple combination mappings. The above case is completely different from the ${f}_{d}\ne 0$ case wherein ${M}_{DP}$ to $t\phantom{\rule{0ex}{0ex}}\forall \phantom{\rule{0ex}{0ex}}{\exists}_{{V}_{r}}$ and ${\exists}_{C}$ are jointly analyzed. If the joint analysis is recommended, then the time requirement is high for which ${V}_{r}$ is alone induced. Based on the $F\left(D\right)$ and ${\exists}_{C}$, the new augmentations are induced provided $\rho \left(C\right)<\rho \left({V}_{r}\right)$ is satisfied. The new designs are introduced for flew detection and recommendation improvements in handling ${t}_{1}$ to ${t}_{k}$. The deep learning paradigm encloses the same for segregating training and representation sessions. This is prominent across different ${\exists}_{C}>{n}_{r}\left(c\right)$ such that the completeness is ensured regardless of any ${V}_{r}$. The final consumer-related output for filling/design augmentation for $R\left(D\right)=1$ or $0$ requires recommendation. For the new detections, this factor is high maximizing ${V}_{r}$ under recommendation time demands.

Fig. 11

Recommendation time analysis.

### 4.5. Flaws

The comparative analysis of the flaw is tabulated in Table 3, which shows the varying designs and detection rates. The proposed method reduces the flaws by identifying the recommendations for representations and consumers through deep learning validations. The conditions based on different input designs are identified for their detection and further representations using the augmentations. This is required to prevent multiple interruptions across the varying conditional analysis. The proposed method detects multiple input designs and their multi-model projection to prevent additional flaws. The proposed method prevents flaw occurrences based on the converging filling or replacement detected. Therefore, the proposed method reduces the flaws in the varying detection rates and designs.

### 4.6. Efficiency ratio

The use of hierarchical perception technologies in the fashion industry has the potential to improve fit and tailoring by gaining insight into consumer tastes and body types. By analysing massive volumes of fashion data for learnt hierarchical characteristics, deep learning might advise and inform designers on design decisions. Designers may find any issues with virtual clothes early on in the process with the help of flaw detection.

Fig. 12 expresses the efficiency ratio. Designers may save time and energy by avoiding revisions required to fix mistakes later on if they discover them early on. With defect detection technologies, designers, manufacturers, and merchants can all work together more effectively, and design teams can communicate and collaborate more effectively. These technologies allow for better teamwork and decision-making by giving precise comments on virtual clothing problems. Finding and fixing mistakes early in the design phase can save money. Businesses may save money and increase profits by cutting out the necessity for costly physical prototype changes and remakes.

Fig. 12

Efficiency ratio analysis.

## 5. Conclusion

This article introduces a flaw detection method in 3D fashion design representation to maximize the adaptability and recommendation ratio. The proposed method classifies the input based on augmentation and existing feature classification to improve the training rate. Dimensional visualization is recommended for multiple augmented inputs during classification and flaw condition validations. In learning, completeness and flaws are distinguished for multiple representations maximizing flaw detections. This process is recurrent post augmentation mitigating the fixations in recommendations. The proposed method organizes the completeness in migrating the training instances for varying multi-model representations to improve both recommendations. The recommendations are provided using the design adaptability verified under varying inputs to ensure flaw rectification. Therefore, the maximum possible combinations in fashion product representation are achieved in this method. The comparative analysis shows that the proposed method maximizes adaptability by 13.43%, the recommendation ratio by 9.56%, and the recommendation time by 10.75 for the varying designs. However, this study has a limitation in finding errors in complex 3D models, which could be a computationally intensive process, particularly in settings with limited resources. This could create problems for systems that identify real or near real-time defects.

## Data availability

Research data are not shared.

## CRediT authorship contribution statement

**Xu Cong:** Writing – original draft. **Wenjia Zhang:** Writing – review & editing, Supervision.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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