Detection of Defects In Fabric

Abstract

Fabric inspection is incredibly significant in textile manufacturing. Fabric defect detection is a key part of product quality assessment in the textile industry. It is important to achieve fast, accurate and efficient detection of fabric defects to improve productivity in the textile industry. Profits of industrialists have been decreased due to fabric defects and cause disagreeable loses. Traditional defect detection methods are conducted in many industries by professional human inspectors who manually draw defect patterns.

However, such detection methods have some shortcomings such as exhaustion, tediousness, negligence, inaccuracy, complication as well as time consuming which cause to reduce the finding of faults. To solve these issues, a framework based on image processing and deep learning has been implemented to automatically and efficiently detect and identify fabric defects.

The most frequently detected defects are missing weft or warp threads, oil stains and holes. The system works according to five steps. It begins by capturing the image, then eliminates parasite information and increases the sharpness of the image-by-image analysis, then perform image classification. After that, YOLOv5 model is implemented for defect detection. Finally real time implementation is done by integrating high resolution camera with the system