Intelligent Garment Detection Using Deep Learning

Joshi, Aniruddha Srinivas; Gupta, Savyasachi; Kanahasabai, Goutham; Ijjina, Earnest Paul

doi:10.1002/9781119792642.ch3

Cited by 1 publication

(1 citation statement)

References 18 publications

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“…However, the garment industry is typically a low-tech and labour-intensive industry. With the rapid development of the economy, the consumption habits of customers are constantly changing and the demand for personalisation is increasing [ 13 ]. In this context, garment companies need to constantly improve the efficiency of their product development in order to cope with the fierce competition in the market [ 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Garment Design Models Combining Bayesian Classifier and Decision Tree Algorithm

Yan

2022

Computational Intelligence and Neuroscience

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With the rapid economic development and rising consumption levels in recent years, people are becoming more and more demanding in terms of style and fashion of clothes. As a result, customer demand for personalised clothing is increasing and the need to respond quickly to consumer demands is also becoming a competitive issue for clothing companies. The automation and intelligence of the garment design and production process is an important part of the implementation of intelligent manufacturing in the garment industry and a necessary way to transform and upgrade the garment industry. Successful clothing styles always have a distinct style identity. The style of the garment can not only conveys the designer's vision but also express the emotional needs of the consumer. In contrast, traditional garment design involves only designers and a single style. With so many styles available, the user has only been able to combine them repeatedly and has not been able to create an innovative design. In addition, apparel design and product development is still a highly empirical task. To be specific, most apparel companies can only respond to a rapidly changing market by increasing the number of designers. However, this blind expansion of staff inevitably leads to increased production costs. As a result, how to effectively develop garment products without relying on the empirical knowledge of garment designers is one of the important issues in achieving intelligent manufacturing in garment enterprises. With the rapid development of computer and network technologies, artificial intelligence, machine learning, and expert systems are widely used in various industries. Nevertheless, the application of these advanced technologies in the field of garment design is still not deep enough. This is mainly due to the uncertainty and imprecision of garment design knowledge. Also, with the rapid development of the fashion industry and the arrival of the trend of personalisation, people's demand for clothing has gradually shifted from mass appeal in terms of comfort and aesthetics to personalisation in terms of self-polishing and temperament. The personalisation of clothing encompasses a wide range of preferences in terms of style and fit. The bottom-up design process and the relatively independent setup of functional modules in traditional clothing technology have prevented the different design levels from being interlinked. This does not reflect the composition of the garment elements in the process of forming features and makes it difficult to grasp the overall design state of the garment. Therefore, in order to address these above issues, this paper proposes a garment design model based on the Bayesian classifier and decision tree algorithm to investigate how computer technologies can be applied to model garment design knowledge. This model can enable inexperienced designers to develop garment products quickly and efficiently to meet the customisation needs of customers, thus enhancing the market competitiveness of garment enterprises.

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Section: Introductionmentioning

confidence: 99%

Garment Design Models Combining Bayesian Classifier and Decision Tree Algorithm

Yan

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

Intelligent Garment Detection Using Deep Learning

Cited by 1 publication

References 18 publications

Garment Design Models Combining Bayesian Classifier and Decision Tree Algorithm

Garment Design Models Combining Bayesian Classifier and Decision Tree Algorithm

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