Combining smart technologies for high quality

Prof. Wong’s team integrates Artificial Intelligence, Big Data, Deep Learning and Machine-vision technologies in “WiseEye,” which enhances the automation of quality control in textile manufacturing. Source: PolyU

Quality assurance is an essential step for any manufacturer. In the textile industry, manual labor, although often unreliable, is generally relied upon for this important step. In order to improve the quality assurance process in the textile industry, the Textile and Apparel Artificial Intelligence (TAAI) Research Team at Hong Kong Polytechnic University (PolyU), and spearheaded by Prof. Calvin Wong, Cheng Yik Hung professor in Fashion of Institute of Textiles and Clothing, developed the “WiseEye.”

Supported by artificial intelligence (AI)-based machine-vision technology, WiseEye is an automated textile inspection system that can be installed in a weaving machine to help manufacturers instantly detect defects in fabrics during the production process, identify the problems and proactively respond to them.

In a six-month trial within a real-life manufacturing environment, WiseEye was able to reduce 90% of the loss and wastage in fabric manufacturing process when compared with human inspection, the research team reports.

How does it provide these great results? Wong explained in a statement: “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 embedded with a high-power LED light bar and a high-resolution charge-coupled device camera, which is driven by an electronic motor and is mounted on a rail to capture images of the whole width of woven fabric during the weaving process. The captured images are pre-processed and fed into the AI-based machine vision algorithm to detect fabric defects. Real-time information gathered throughout the detection process will be sent to the computer system, and analytical statistics and alerts can be generated and displayed as and when needed.”

“WiseEye” can detect around 40 types of common fabric defects with exceptionally high accuracy. Source: PolyU

The research team applied big data and deep learning technologies into the inspection system. By inputting data from thousands of yards of fabric, the system is trained to detect 40 common fabric defects with accuracy resolution of up to 0.1 mm/pixel.

The WiseEye system can be applied to most types of fabrics with different weaving structures and solid colors; however, the team plans to further train the system and extend its abilities to detect defects in fabrics with patterns, with the ultimate goal of covering all common fabrics in five years’ time.

“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. Our 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,” Wong added.

Quality product means happy customers and happy customers means good business. It’s innovations that combine smart technologies like the WiseEye that, when implemented, can allow businesses to be that much more efficient and successful.