An intelligent defect detection system for warp-knitted fabric

Abstract

In textile factories, the most typical warp-knitted fabric defects include point defects, holes, and color differences. Traditional manual inspection methods are inefficient for detecting these defects. Existing intelligent inspection systems often have a single function. Factories require a real-time inspection system that can detect common defects and color difference. The YOLO (you only look once) neural network is faster than the two-stage neural network and has lower hardware requirements. The system’s color difference detection algorithm compares the color difference between the standard image and the image to be measured and records where the color difference value is exceeded. Finally, the comparison of the factory application proves that the designed system has good real-time performance and accuracy and can meet the fabric inspection requirements of warp-knitted fabric factories.