This invention relates to the field of defect detection technology, specifically to a method for improving the YOLOv8 network and its application in
strip steel surface defect detection. Based on YOLOv8, this invention first proposes an improved coordinate attention mechanism, the three-channel coordinate attention mechanism TCCA. Addressing the overly simplistic offset
mask generation method in DCNv2, which leads to insufficient deformation modeling capabilities, this invention proposes deeply embedding TCCA into DCNv2. Furthermore, this invention introduces a global attention mechanism to improve the model's ability to extract global features. The invention also introduces a BiFPN structure and dynamic serpentine
convolution to enhance the model's multi-scale
feature fusion capabilities. MDPioU replaces the original
loss function of YOLOv8, solving the problem of loss of effectiveness due to identical aspect ratios in predicted bounding boxes, while increasing convergence speed and localization ability. Extensive comparative and
ablation experiments on the NEU-DET dataset demonstrate that the
improved algorithm of this invention achieves higher defect detection accuracy and faster speed.