Ship target and key parts detection algorithm based on YOLOv5

被引：0
|
作者：
Qian K. [1 ,2 ]
Li C. [1 ]
Chen M. [1 ]
Wang Y. [1 ]
机构：
[1] College of Coastal Defense Force, Naval Aeronautical University, Yantai
[2] Unit 32127 of the PLA, Dalian
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2022年 / 44卷 / 06期
关键词：
Bi-directional feature pyramid network; Exponential linear unit function; Stochastic pooling; YOLOv5;
D O I：
10.12305/j.issn.1001-506X.2022.06.07
中图分类号：
学科分类号：
摘要：
In order to improve the detection and recognition success rate of the surface warship target in visible light images, an algorithm based on YOLOv5 is proposed. The spatial pyramid pooling network based on stochastic pooling is used for pooling operation, and the bi-directional feature pyramid network is used for feature fusion. At the same time, the exponential linear unit function is used as the activation function to further accelerate the convergence speed and improve the robustness of the model, so as to realize the rapid and accurate recognition of surface ship targets and key parts of the ship. Through the experimental verification on the data set of the ship target and its key parts, compared with the mainstream target detection methods, the recognition accuracy is improved in varying degrees. Compared with the original YOLOv5s model, the mean average precision is improved by 3.03%, and the speed is improved by 2 FPS. The model maintains the lightweight characteristics of YOLOv5 and has a good prospect in application deployment. © 2022, Editorial Office of Systems Engineering and Electronics. All right reserved.
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页码：1823 / 1832
页数：9
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