CO_YOLO:Marine Biological Object Detection Based on Improved YOLOv5
Due to the complexity of underwater environment,objects overlap and occluding ex-ist.In order to solve this problem,this paper improved on the basis of YOLOv5s and proposed CO_YOLO.By replacing C3 with C3_ODConv,the computational load of the model was re-duced,and CBAM attention mechanism was added to enhance feature extraction capability of the model.The experimental results show that CO_YOLO has a good effect on Marine biological target detection,and has a better effect than YOLOv5s on URPC2020 data set,with mAP50 reaching 0.828 and GFLOPs reaching 16.5.
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