基于SAE-YOLOv5的机载红外UXO目标检测方法  

Airborne infrared UXO target detection method based on SAE-YOLOv5

作　　者：刘子玉 赵旭[1] 李连鹏[1] 许学平 LIU Zi-yu;ZHAO Xu;LI Lian-peng;XU Xue-ping(Beijing Key Laboratory of High Dynamic Navigation Technology,Beijing Information Science and Technology University,Beijing 100192,China;Beijing Honda Hechuang Defense Technology Research Institute Co.,Ltd.,Beijing 100048,China)

机构地区：[1]北京信息科技大学高动态导航技术北京市重点实验室,北京100192 [2]北京宏大和创防务技术研究院有限公司,北京100048

出　　处：《激光与红外》2025年第2期288-295,共8页Laser & Infrared

基　　金：国家重点研发计划项目(No.2020YFC1511702);高动态导航技术北京市重点实验室项目;“慧眼行动”创新成果转化应用项目(XXX新型多模智能探测系统)资助。

摘　　要：针对无人机在多种植被环境下检测地面未爆弹的光学精度低、误检率高等问题,提出了一种基于未爆弹红外特征的YOLOv5未爆弹检测方法。首先对采集到的未爆弹目标数据进行重建,然后引入ECA注意力机制以提高识别精度;同时引入ASPP空洞空间金字塔池化以提高识别效率,并使用CIoU_NMS作为预测框筛选依据。实验证明,在多组不同植被环境的鸟瞰UXO目标红外数据集上,SAE-YOLOv5算法相较于原YOLOv5算法模型,在UXO目标方面精确率由83%提高至87%,平均精度均值从83.6%提升至85%。该算法在文中所述的四种复杂背景下都能有效检测UXO目标,并且漏警率低。To address the problems of low optical precision and high false alarm rate in detecting unexploded ordnance(UXO)on the ground in various vegetation environments with drone-based optical detection,a UXO detection method based on the infrared features of unexploded ordnance using YOLOv5 is proposed in this paper.Firstly,the target data of unexploded ordnance is reconstructed,and the ECA attention mechanism is introduced to improve the recognition accuracy.At the same time,the ASPP hole space pyramid pooling is introduced to improve the recognition efficiency,and the CIoU_NMS is used as the prediction box selection criterion.The experimental results show that on the bird′s eye view UXO target infrared data set in multiple groups of different vegetation environments,the SAE-YOLOv5 algorithm has an improvement in UXO target precision from 83%to 87%,and the average precision mean is improved from 83.6%to 85%,compared with the original YOLOv5 algorithm model.The algorithm is effective in detecting UXO targets in the four complex backgrounds mentioned in the paper,and with a low false alarm rate.

关 键 词：植被环境 未爆弹目标 机载探测 YOLOv5 红外特征 

分 类 号：TH745[机械工程—光学工程] TP73[机械工程—仪器科学与技术]