Light-Trans YOLO: A Lightweight Network Based on Transformer for Defect Detection of Industrial Lace Surface

Abstract

Lace surface Defect detection has always been a crucial step in the industrial production of lace products. However, due to the complex texture and deforma-bility of lace, as well as the difficulty of distinguishing minor defects from normal images. Therefore, the detection of defects on lace surfaces is a challenging but rarely studied task. In this paper, we propose a new lightweight detection framework , Light-Trans YOLO, to detect lace surface defects. First, our backbone network uses the lightweight network C3 GhostNet. In addition, to obtain more complete global information, we add the lightweight Mobile Transformer Block (MTB) to the backbone network. Then we use the proposed standard deep-wise separable convolution (SDSConv) and SDSBottleneck to design a new neck and add Coordinate Attention (CA) at the end, which overcomes the problem of information loss of deep separable convolution and extracts more effective information. In the training, we propose the loss function ϵSIoU, which can improve the separability of defective and normal samples. We conduct experiments on the industrial lace surface defect dataset collected in lace production sites, and the experiments prove that the mAP of our model is 96.6%, which is 7.7% higher than YOLOV5s, and the FPS and F1-score of the model reaches 50.3 and 0.93, which indicates that our model has a great trade-off between detection accuracy and speed.