A Lightweight Detector Based on Attention Mechanism for Fabric Defect Detection

Abstract

Defects on fabric surfaces are difficult to identify owing to unsuitable computing devices, highly complex algorithms, small size, and high degree of integration with the fabric. To this end, this study proposes a lightweight fabric defect-detection network, YOLO-SCD, based on attention mechanism. The introduction of depth-wise separable convolution and the attention mechanism enhanced the capacity of the neck network to extract the defective features and increased the detection speed of the overall network. The extensive experimental results revealed that YOLO-SCD achieved an average accuracy of 82.92%, effective improvement of 8.49% in mAP, and an improvement of 37 fps compared to the original YOLOv4 on a standard fabric defect dataset. By leveraging its swift detection speed and high efficiency, YOLO-SCD excels in both the general fabric defect category and the difficult-to-detect fabric. Overall, it exhibited strong performance in detecting both minor flaws and flaws with high fabric integration. Furthermore, the proposed model was extended to steel datasets with similar characteristics.

Dictionary Learning Based Adaptive Defect Detection In Complex Fabric Textures

“…Automated inspection system is required to maintain the quality of fabric. The detection system can be implemented through deep learning [1], [2], [3], [4], [5], [6], [7], [8], spectral estimation [9], vision [10], [11], [12], [13], and dictionary, [14] [15] based learning strategies. Boshan Shi et al proposed a decomposition model with gradient information and noise regularization (G-NLR) to determine the edge position of the image [16].…”