Hong Kong Polytechnic University develop WiseEye intelligent fabric defect detection system | Automation.com

Hong Kong Polytechnic University develop WiseEye intelligent fabric defect detection system

Hong Kong Polytechnic University develop WiseEye intelligent fabric defect detection system

October 18, 2018 - The Hong Kong Polytechnic University (PolyU) recently developed an intelligent fabric defect detection system, called “WiseEye”, which leverages technologies including Artificial Intelligence (AI) and Deep Learning in the process of quality control (QC) in textile industry.

Supported by AI-based machine-vision technology, the novel “WiseEye” can be installed in a weaving machine to help fabric manufacturers to detect defects in the production process. Through the automatic inspection system, the production line manager can detect the defects, thus helping them to identify the cause of the problems and fix them. 

“WiseEye” is developed by the Textile and Apparel Artificial Intelligence (TAAI) Research Team, which is spearheaded by Prof Calvin Wong, Cheng Yik Hung Professor in Fashion of Institute of Textiles and Clothing, PolyU. 

The research team has applied Big Data and Deep Learning technologies in “WiseEye”. By inputting data of thousands yards of fabrics into the system, the team has trained “WiseEye” to detect about 40 common fabric defects with exceptionally high accuracy resolution of up to 0.1 mm/pixel.

At the moment, “WiseEye” can be applied to most types of fabrics with different weaving structures and solid colours. The research team plans to further train and extend the system to detect defects in fabrics with more challenging patterns, such as complicated strip and check patterns. The ultimate target is to cover all common kinds of fabric in five years’ time.

Prof Wong and the TAAI research team have been conducting fundamental and applied research on AI, computer vision and machine learning, specifically for the fashion and textile industry since 2012. The team has earlier introduced the first-of-its-kind “FashionAI Dataset” which integrates fashion and machine learning for systematic analysis of fashion images through the use of AI.

Areas covered by their other projects include intelligent textile material and apparel quality inspection, large-scale fabric swatch and fashion image searching and fashion sales forecasting. The team has also collaborated with various local and international companies in a number of research projects and published research articles in world-leading journals, including IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Cybernetics, and IEEE Transactions on Image Processing. Some of the articles are ranked by Essential Science Indicators as the top 1% of the most cited articles in related fields.

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