Fabric Defect Detection Algorithm Based On Improved Yolov5 In order to address the issues of real time performance and the low dependency between feature channels in fabric defect detection networks, this paper proposes. This study designs a lightweight fabric defect detection algorithm based on yolov7 tiny, called yolov7 tiny mgck.
Figure 1 From Efficient Fabric Defect Detection Algorithm Based On Addressing the challenges of diverse fabric defect types, complex fabric textures, and concealed small target defects, this paper improves the overall performance of fabric defect detection algorithms by enhancing the yolov8n algorithm. Fabric defect detection is an important part of the textile industry, aiming at the problems of many types of fabric defects, small size defects and unbalanced samples, an improved yolov5 fabric defect detection algorithm, fd yolov5, was proposed. Based on the advantages of yolov8n in detection speed and efficiency, this paper proposes an improved algorithm to address challenges such as inaccurate localization and false positives caused by complex fabric textures and variable defect sizes. In the finished product stage, an automated fabric surface defect detection algorithm is used for a comprehensive visual inspection, with a focus on issues such as holes and oil stains, ensuring that the garment meets the quality standards for shipment.
Figure 3 From Efficient Fabric Defect Detection Algorithm Based On Based on the advantages of yolov8n in detection speed and efficiency, this paper proposes an improved algorithm to address challenges such as inaccurate localization and false positives caused by complex fabric textures and variable defect sizes. In the finished product stage, an automated fabric surface defect detection algorithm is used for a comprehensive visual inspection, with a focus on issues such as holes and oil stains, ensuring that the garment meets the quality standards for shipment. This study designs a lightweight fabric defect detection algorithm based on yolov7 tiny, called yolov7 tiny mgck. its objectives are to improve the performance of fabric defect detection against complex backgrounds and to find a balance between the algorithm’s lightweight nature and its accuracy. Based on the transformer structure, we optimize the yolov5 v6.1 algorithm with the swin transformer as the backbone, and the introduction of a multiwindow sliding self attention mechanism complements the convolutional network to improve classification accuracy. A fabric defect detection network based on dynamic enhancement of texture features is proposed to solve the problem of identifying defects from background textures in complex environments. This paper proposes a defect detection system based on improved yolo v4, which greatly improves the detection ability of minor defects.
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