Improved YOLOv8 garment sewing defect detection method based on attention mechanism

For the garment sewing defect detection method, this paper proposes an improved YOLOv8-FPCA scheme based on the YOLOv8 algorithm, which improves the YOLOv8 target detection head to enhance the information extraction of small target defects, then introduces Focal Loss to optimize the loss function to guide the network to better handle target data sets with different difficulties and imbalances. Finally, the attention mechanism CA is added to the YOLOv8 network structure to achieve multi-scale feature fusion extraction, and the attention mechanisms CABM and SENet are added at the same locations for experimental comparison. The results show that after increasing the attention mechanisms of CBAM and SENet, the mAP@0.5 model increased by 1.7 % and 1.9 % respectively. The CA attention mechanism emphasizes the importance of location information, and the model has better accuracy and recall after adding the CA attention mechanism, with a 3.7 % increase in mAP@0.5, indicating that YOLOv8-CA has better performance in sewing defect detection.