Abstract
The fast detection and classification of underwater targets is a key issue in the operation of intelligent underwater robots. In order to improve the detection speed of underwater targets and reduce the missed detection rate of small targets, an improved YOLOv3 algorithm named YOLOv3-Marine is proposed. The network parameters were reduced and the detection speed was increased due to improving the YOLOv3 network structure. The residual module was optimized to improve the feature extraction capabilities of the network, which greatly reduced the rate of missed detection in the case of densely distributed targets. Finally, the prediction scale module and the loss function were improved to increase the detection accuracy of small underwater targets. The final experimental results showed that the proposed YOLOv3-Marine algorithm has a higher detection speed and detection accuracy than the YOLOv3 algorithm.
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