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.

中文翻译：

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基于改进YOLOv3算法的复杂场景水下目标检测与分类

摘要。水下目标的快速检测和分类是智能水下机器人运行中的关键问题。为了提高水下目标的检测速度，降低小目标的漏检率，提出了一种改进的YOLOv3算法YOLOv3-Marine。由于改进了YOLOv3网络结构，减少了网络参数，提高了检测速度。对残差模块进行了优化，提高了网络的特征提取能力，在目标密集分布的情况下，大大降低了漏检率。最后，改进了预测尺度模块和损失函数，提高了水下小目标的检测精度。