科技创新与应用2024，Vol.14Issue(26)：54-59,6.DOI:10.19981/j.CN23-1581/G3.2024.26.011
基于改进YOLOv5的Video SAR动目标检测算法
白浩琦 1李和平2
作者信息
- 1. 中国科学院空天信息创新研究院,北京 100094||中国科学院大学 电子电气与通信工程学院,北京 100049
- 2. 中国科学院空天信息创新研究院,北京 100094
- 折叠
摘要
Abstract
The shadows of moving targets in video synthetic aperture radar(Video SAR)images can reflect their real positions,and an improved YOLOv5 model is proposed to address the problems of low detection probability and false alarms in the current method of detecting shadows using deep learning to detect moving targets.The algorithm is based on the YOLOv5 framework.On the one hand,it adds a small target detection layer,introduces a three-dimensional attention mechanism that takes into account the channel and space,and adjusts the loss function calculation method to the intersection over union based on minimum point distance method to improve the network's ability to detect shadows of moving targets;on the other hand,it adds the road extraction as a decision condition to reduce the interference of the shadows of static objects outside the road in detecting the shadows of moving targets.After experimental verification,the algorithm improves the average accuracy by 7.12%compared with YOLOv5,which meets the requirements of moving target detection.关键词
视频SAR/运动目标检测/深度学习/YOLOv5/阴影检测Key words
Video SAR/moving target detection/deep learning/YOLOv5/shadow detection分类
信息技术与安全科学引用本文复制引用
白浩琦,李和平..基于改进YOLOv5的Video SAR动目标检测算法[J].科技创新与应用,2024,14(26):54-59,6.
