MSA-YOLO:Real-time Segmentation Algorithm for Skin Surface Defects
In response to the current problems of low efficiency and insufficient accuracy in manual in-spection of skin surface,a real-time segmentation algorithm MSA-YOLO for skin surface defects is pro-posed.The multi-scale attention MSA module replaces the C2f module of the YOLOv8-seg network back-bone to improve feature representation while achieving network lightweight.The eSE attention mechanism layer is added to the small target detection layer of the network to enhance the detection ability of small target defects.Finally,the Inner-CIOU loss function is used instead of the original CIOU loss function,and the auxiliary bounding box is used to accelerate the convergence process of samples.A dataset contai-ning five typical defects on the skin surface is created for validation.The results show that the MSA-YO-LO segmentation algorithm improves the average precision(mAP)of the target box and mask by 3.7%and 5.3%respectively compared to the original algorithm,and the detection speed is increased by 9.1%.Compared with other popular real-time segmentation algorithms at this stage,it has certain per-formance advantages and is of significance for achieving automated segmentation of skin surface defects.
万方数据
维普
