Defect detection algorithm for fabric based on deformable convolutional network

Abstract

The detection of fabric defects is an important aspect of textile quality management. This paper proposes an algorithm based on YOLOv3 and deformable convolutional network to solve the problems of low accuracy, high rate of missed and false detection, and high labor cost existing in traditional manual detection methods. First, data enhancement is adopted to address the problem of imbalanced categories of defective samples in the dataset. Then, the Resnet101 model is used to extract the features, and the original convolution operation is replaced by deformable convolution to balance the accuracy and speed of the model. Finally, the focal loss function is introduced to solve the insensitivity problem of the model to difficult defect samples. Experimental results show that this method can quickly and accurately detect defects on the surface of fabrics. In real-time detection, the average accuracy of this method is 8.3% higher than that of YOLOv3. The average accuracy of multiple categories of detection reaches more than 90%, and the detection effect of small defect targets, such as three silk, is also relatively better.

A Lightweight Detector Based on Attention Mechanism for Fabric Defect Detection

“…As observed from the table, the present experimental results were 5.62% more accurate than those reported by the first-place winner of the Tianchi industrial competition. For validation, we compared the current experimental results with those obtained by Liu et al [23,24,32], who applied the fabric defect dataset of Tianchi. As observed, the present model displayed adequate results for individual defects, and the detection accuracy of large defects such as stain, hundred feet, and other difficult-to-detect defects such as abrasion mark were optimal compared to other studies.…”

“…However, its training set contained only 200 sheets with three categories of defects, which yielded weak generalization and inferior robustness of the model. Luo et al [23] proposed a fabric defect-detection method based on YOLOv3 combined with deformable convolution, which provided adequate detection results for 17 types of fabric defects. However, the model size was excessively large for practical applications in textile mills.…”

A Small Intestinal Stromal Tumor Detection Method Based on an Attention Balance Feature Pyramid

“…In the comparison of the same model, it is compared with the mainstream attention mechanism SE attention mechanism, CBAM attention mechanism, and DCN, respectively. By combining with ResNet101 network with deformable convolutional DCN [ 30 ], it is proved that the method in this paper is compatible with networks with different structures.…”