Research on Textile Tiny Defective Targets Detection Method Based on YOLO-GCW

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In the textile quality control system, textile defect detection occupies a central position. In order to solve the problems of numerous model parameters, time-consuming computation, limited precision, and accuracy of tiny features of textile defects in the defect detection process, this paper proposes a textile defect detection method based on the YOLO-GCW network model. First, in order to solve the problem of detection accuracy of tiny defective targets, the CBAM (Convolutional Block Attention Module) attention mechanism was incorporated to guide the model to focus more on the spatial localization information of the defects. Meanwhile, the WIoU (Weighted Intersection over Union) loss function was adopted to enhance model training as well as to improve the detection accuracy, which can also provide a more accurate measure of match between the model-predicted bounding box and the real target to improve the detection capability of tiny defect targets. Consequently, in view of the need for performance optimization and lightweight deployment, the Ghost convolution structure was adopted to replace the traditional convolution for compressing the model parameter scale and promoting the detection speed of complex texture features in textiles. Finally, numerous experiments proved the positive performance of the presented model and demonstrated its efficiency and effectiveness in various scenes.