舰船电子工程2024，Vol.44Issue(4)：28-33,6.DOI:10.3969/j.issn.1672-9730.2024.04.007
基于改进YOLOv5的军事目标识别方法
A Military Target Detection Method Based on Improved YOLOv5
摘要
Abstract
To address the problem of false detection and missed detection due to background interference and small scale of military targets in battlefield environment,a military target recognition method CB-YOLOv5 based on improved YOLOv5 is pro-posed.The feature extraction backbone network is reconstructed by using coordinate attention mechanism to enhance the feature ex-traction capability of the network for military targets in complex background.BiFPN is introduced in the feature fusion network to re-duce the loss of shallow feature information and improve the weak targets can be detected.Experiments under the self-built dataset show that the improved algorithm mAP reaches 93.8%,which is 3.5%better than the original model,and can effectively identify multi-scale military targets in the battlefield environment.关键词
目标识别/YOLOv5/注意力机制/特征融合Key words
target detection/YOLOv5/attention mechanism/feature fusion分类
信息技术与安全科学引用本文复制引用
万晓刚,王伟..基于改进YOLOv5的军事目标识别方法[J].舰船电子工程,2024,44(4):28-33,6.基金项目
2021年中国高校产学研创新基金项目(编号:2021ALA02002)资助. （编号:2021ALA02002）
