togetherforacleanocean - PolyU comes out with intelligent quality control

The Hong Kong Polytechnic University (PolyU) has come out with an intelligent fabric defect detection system▪◇△=◆, called WiseEye-▽▷●◁, which leverages advanced technologies including Artificial Intelligence (AI) and Deep Learning for quality control (QC) in textile industry☆▼. The system effectively minimises the chance of producing substandard fabric by 90 per cent☆△▷●.
The system substantially reduces loss and wastage in the production●▼•. It also 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 has been 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▲□■•.
Textile manufacturers currently rely on human efforts to randomly inspect the fabric by naked eyes▪◇▲. Due to human factors such as negligence or 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 has been a great challenge to the industry□•△.
Wong said○=•☆◇, &quot●○…;Wise Eye 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 alert can be generated and displayed as and when needed□=☆.&quot◆◇▽=;
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○●★.
&quot▷=▼;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 &#39◆▪;WiseEye&#39○▪◆=; 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•□•,&quot○●; added Wong◇○▷△•.
&quot◇◆;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 per cent of the loss and wastage in fabric manufacturing process when compared with 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&#39◇=; time◇☆.
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 &quot▼▽;FashionAI Dataset&quot▪•; which integrates fashion and machine learning for systematic analysis of fashion images through the use of AI▽▽▼. The Dataset helps advance the fashion industry and develop a new mode for fashion retail▪◆◇☆◇. (SV)

Leave a Reply