We are creating some awesome events for you. Kindly bear with us.

PolyU team develops smart fabric detection system

According to a recent press release, The Hong Kong Polytechnic University (PolyU) announced that it has developed an intelligent fabric defect detection system, called “WiseEye”, which leverages advanced technologies including Artificial Intelligence (AI) and Deep Learning in the process of quality control (QC) in the textile industry.

The system effectively minimises the chance of producing substandard fabric by 90%, thus substantially reducing loss and wastage in the production. It helps to save manpower as well as enhance the automation management in the textile manufacturing.

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

“WiseEye” is developed by the Textile and Apparel Artificial Intelligence (TAAI) Research Team, spearheaded by two professors from the department of Fashion of Institute of Textiles and Clothing at PolyU.

Textile manufacturers currently rely on human efforts to randomly inspect the fabric by naked eyes. Due to human factors such as negligence or physical fatigue, defect detection by human labour is usually inconsistent and unreliable.

Textile manufacturers also attempted to use some other fabric inspection systems, but those systems were not able to meet the industry needs. Ensuring quality in the fabric production becomes a great challenge to the industry.

One of the professors stated that ‘Wise Eye’ is a unique AI-based inspection system; it is also 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.

He added that 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 alert can be generated and displayed as and when needed.

The research team has applied Big Data and Deep Learning technologies in “WiseEye”. By inputting data on thousands of 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.

It was noted that since there are 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.

The team’s 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.

“WiseEye” has been put on trial for over six months in a real-life manufacturing environment. Results show that the system is able to reduce 90% of the loss and wastage in the fabric manufacturing process when compared with the traditional human visual inspection. That means the system helps cut down production cost while enhancing production efficiency at the same time.

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.

Send this to a friend