Abstract
Fabric defect inspection is necessary and essential for quality control in the textile industry. Traditionally, fabric inspection to assure textile quality is done by humans, however, in the past years, researchers have paid attention to PC-based automatic inspection systems to improve the detection efficiency. This paper proposes a novel automatic inspection scheme for the warp knitting machine using smart visual sensors. The proposed system consists of multiple smart visual sensors and a controller. Each sensor can scan 800 mm width of web, and can work independently. The following are considered in dealing with broken-end defects caused by a single yarn: first, a smart visual sensor is composed of a powerful DSP processor and a 2-megapixel high definition image sensor. Second, a wavelet transform is used to decompose fabric images, and an improved direct thresholding method based on high frequency coefficients is proposed. Third, a proper template is chosen in a mathematical morphology filter to remove noise. Fourth, a defect detection algorithm is optimized to meet real-time demands. The proposed scheme has been running for six months on a warp knitting machine in a textile factory. The actual operation shows that the system is effective, and its detection rate reaches 98%.
Highlights
Defect inspection is a quality control process that identifies and locates deficiencies in the fabric manufactured in the textile industry
This study proposes an automatic inspection scheme using smart visual sensors, which are used in the detection of fabric defects in a warp knitting machine
The proposed fabric defect inspection system based on smart visual sensor was installed on the warp knitting machines and operated successfully
Summary
Defect inspection is a quality control process that identifies and locates deficiencies in the fabric manufactured in the textile industry. An automatic inspection system becomes an effective way to improve textile quality because of the progress of machine vision technology Research in this field has been carried out, and some PC-based prototype systems have been developed. Experiments showed the effectiveness of his method for plain and interlocked weft-knitted fabrics with holes, dropped stitches, and course mark defects. All these schemes employed PC-based architectures that consisted of a lighting system, cameras, frame grabbers, and host computers. This study proposes an automatic inspection scheme using smart visual sensors, which are used in the detection of fabric defects in a warp knitting machine
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
