Defect detection of zipper tape based on lightweight deep learning network

Zipper tapes are essential in the garment industry, with defects significantly impacting the appearance of fabric products. Current methods for inspecting zipper tapes suffer from low detection accuracy. This paper proposes a lightweight, deep learning-based defect detection method using YOLOv5. The method includes a newly designed C3-CM module that integrates the Convolutional Block Attention Module (CBAM) into the backbone network, enhancing defect attention and feature extraction capabilities. Additionally, the lightweight GSConv convolution reduces parameter count while maintaining accuracy. The VoV-GSCSP module constructs deep-level feature fusion to boost the network's expressive power. Using the Content-Aware Reassembly of Features (CARAFE), the method leverages underlying semantic information to predict and reorganize kernels, thereby enhancing upsampling effectiveness. Experimental results indicate that this method significantly improves accuracy compared to traditional approaches and outperforms current state-of-the-art algorithms in both accuracy and speed.