Research on fabric defect detection method based on lightweight network

Abstract

Due to the complexity of fabric texture, the diversity of defect types and the high real-time requirements of textile enterprises, fabric defect detection is faced with considerable challenges. At present, fabric defect detection algorithms based on deep learning have achieved good results, but there are still some key problems to be solved. Firstly, due to the complex construction of deep learning models and high network complexity, it is difficult to meet the real-time requirements of industrial sites, which limits its application in industrial sites. Secondly, in the face of textile enterprises’ requirements for detection accuracy, how to achieve fabric defect detection through a simpler network model, so as to better balance the accuracy and complexity of deep learning models is a major challenge for textile enterprises and academic researchers. In order to solve these problems, a fabric defect detection method based on lightweight network is proposed in this paper. This method takes lightweight network YOLOv5s model as the infrastructure, integrates Convolution Block Attention Module and Feature Enhancement Module in Backbone part and Neck part respectively, and modifies the loss function of YOLOv5s to CIoU_Loss. Compared with the original YOLOv5s, it makes up for the lack of information extraction ability of the network, improves the speed of model inference and the speed and accuracy of prediction box regression. It provides technical support for the application of lightweight network model in industrial field. The performance of the model is tested by using raw fabric and patterned fabric data sets on the deep learning workstation platform. The experimental results show that when the IoU threshold is 0.5, the mean Accuracy Precision mAP of raw fabric and pattern fabric is 86.4% and 75.8%, respectively, which increases by 7.6% and 1.7% compared with the original YOLOv5s algorithm. The average detection speed is as high as 102 FPS, which can meet the real-time requirement of industrial field target detection.