数字海洋与水下攻防2024，Vol.7Issue(5)：529-535,7.DOI:10.19838/j.issn.2096-5753.2024.05.009
基于连续帧与注意力机制的水下小目标自主检测算法
Underwater Small Target Detection Based on Continuous Frame and Attention Mechanism
蔡自清 1王力 1梁镜 1李孟霏 1徐凯凯1
作者信息
- 1. 中国船舶集团有限公司第七一〇研究所,湖北 宜昌 443003||清江创新中心,湖北 武汉 430200
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摘要
Abstract
In order to solve the problems of few target features of underwater small targets collected by sonar,and poor performance of conventional target detection algorithm,an improved continuous frame recognition algorithm based on YOLOv5 is proposed.The algorithm uses a continuous frame data extraction module and a lightweight channel spatial attention module to extract sonar continuous frame information,which improves the recognition ability of YOLOv5 algorithm.The lake experimental results based on the forward-looking sonar time series dataset show that the accuracy of the algorithm is improved by 13.7%,and the reasoning time is basically unchanged.The improved algorithm is expected to be applied to the autonomous detection of underwater small targets.关键词
YOLOv5/小目标自主检测/连续帧信息/注意力机制Key words
YOLOv5/underwater small target detection/continuous frame information/attention mechanism分类
信息技术与安全科学引用本文复制引用
蔡自清,王力,梁镜,李孟霏,徐凯凯..基于连续帧与注意力机制的水下小目标自主检测算法[J].数字海洋与水下攻防,2024,7(5):529-535,7.
