Underwater targets detection and classification in complex scenes based on an improved YOLOv3 algorithm
Underwater targets detection and classification in complex scenes based on an improved YOLOv3 algorithm
复制标题
基于改进YOLOv3算法的复杂场景水下目标检测与分类
DOI：
10.1117/1.jei.29.4.043013
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发表时间：
2020-07-01
影响因子：
1.1
通讯作者：
Huang, Yuxuan
中科院分区：
文献类型：
--
作者：
Shi, Tingchao;Liu, Mingyong;Huang, Yuxuan
摘要。水下目标的快速检测与分类是智能水下机器人运行的关键问题。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，命名为YOLOv3- marine。通过改进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，大大降低了目标分布密集情况下的漏检率。最后，对预测尺度模块和损失函数进行了改进，提高了水下小目标的探测精度。最终的实验结果表明，所提出的YOLOv3- marine算法比YOLOv3算法具有更高的检测速度和检测精度。
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.