SAR ship detection based on YOLOv5
Tian Ming, Yanwei Ju · Third International Conference on Computer Vision and Data Mining (ICCVDM 2022) · 2023
For the task of SAR ship detection , improvements are made on the basis of YOLOv5. Considering the ship target characteristics, the loss function is improved. And the coordinate attention mechanism (CA) is added to the backbone. Finally, a layer of feature fusion branches is added to the path aggregation network (PANet). Contrast with unchanged YOLOv5 detection network, this improvement increases the precision rate from 93.5% to 96.1%, the recall rate from 93.4% to 95.3%, and the mAP from 93.9% to 97.3%. The network detection performance has been significantly improved.