Researchers develop AI-based quality control for textile industry

BDApparelNews Desk
23 October 2018  

The AI-based machine for fabric detection. Photo Courtesy: Hong Kong Polytechnic University

The AI-based machine for fabric detection. Photo Courtesy: Hong Kong Polytechnic University

The Hong Kong Polytechnic University has lately developed an intelligent fabric detection system called, ‘WiseEye’, that uses smart technologies like Artificial Intelligence (AI) and Deep Learning in the process of quality control (QC) in textile industry.

The system effectively minimises the chance of producing inferior quality fabric by 90 per cent, thus, substantially reducing the losses and wastages in the production. Moreover, it also helps to save the manpower as well as enhances the automation management in the textile manufacturing, says a press release of the university.

‘WiseEye’ is developed by the Textile and Apparel Artificial Intelligence (TAAI) Research Team, which is headed by Prof. Calvin Wong from Institute of Textiles and Clothing, PolyU.

Prof. Calvin Wong explained, “WiseEye is a unique AI-based inspection system that satisfies the requirements of textile manufacturers. It is an integrated system with a number of components that perform different functions in the inspection process.”

The system is supported by AI based machine vision technology and can be installed in a weaving machine in order to help the fabric manufacturers to detect the defects instantly in the production process. With the help of automatic inspection system, the production line manager can easily detect the defects, identify the cause of problems, and fix them instantly.

It is embedded with high power LED light bar and high-resolution charge-coupled device camera which is further driven by an electronic motor, mounted on rail to capture the images of the whole width of woven fabric during the weaving process.

Further, these captured images are pre-processed and fed into the AI-based machine vision algorithm so as to detect the fabric defects. All the real time information gathered throughout the detection process is forwarded to the computer system, and analytical statistics and alert can be generated and displayed as and when needed.

The research team has applied ‘Big Data’ along with ‘Deep Leaning’ techniques in WiseEye. They have input the data of thousands of yards of fabrics into the system and has trained it to detect about 40 common defects with exceptionally high accuracy resolution of upto 0.1mm per pixel.

Prof Wong, said, “In view of the numerous fabric structures that give great variations in fabric texture and defect types, automatic fabric defect detection has been a challenging and unaccomplished mission in the past two decades.” He added that the innovative introduction of AI, Big Data, and Deep Learning technologies into ‘WiseEye’ not only is a technological breakthrough that meets the industry needs, but also marks a significant milestone in the quality control automation for the traditional textile industry.

According to PolyU, the system is still under trial for six months in a real time manufacturing environment. Also, the results show that the system is able to reduce 90 per cent of the loss and wastage in fabric manufacturing process when compared with traditional human visual inspection. That means, it helps cut down production cost while enhancing production efficiency at the same 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 developed ‘FashionAI Dataset’, which integrates fashion and machine learning for systematic analysis of fashion images through the use of AI.