多尺度互交叉注意力改进的单无人机对地伪装目标检测定位方法

Multi scale cross attention improved method of single unmanned aerial vehicle for ground camouflage target detection and localization

为了提升无人机对地伪装目标探测能力,本文提出了多尺度互交叉注意力改进的单机对地目标检测定位方法。首先,设计了一种多尺度互交叉注意力模块,在原始多尺度金字塔基础上,进行互交叉注意力增强,提升对伪装目标的边界区分能力;其次,搭建了开源无人机目标检测定位系统,通过融合无人机载定位模块、惯导传感器和光电吊舱等数据,在获取目标图像位置后对其空间位置进行解算;最后,自行构建了丛林伪装数据集进行了相关实验验证。实验结果表明,该方法在典型伪装场景下对地目标平均检测精度(mAP)为70.2%,相较于改进前提升5.7%,且能有效输出目标与无人机(UAV)的方位距离,算法平均运行效率可达29.4 fps,满足UAV对地目标检测定位的实时性需求。

To enhance the detection ability of unmanned aerial vehicles(UAV)in ground camouflage targets,this article proposes a multi-scale cross attention improved single machine ground camouflage target detection and localization method.Firstly,a multi-scale cross attention module is designed to enhance cross attention based on the original multi-scale pyramid.The ability to distinguish the boundaries of camouflaged targets is enhanced.Secondly,an open-source drone target detection and positioning system is established,which integrates data such as drone carrier positioning modules,inertial navigation sensors,and optoelectronic pods to calculate the spatial position of the target image after obtaining its position.Finally,a jungle camouflage dataset is constructed and validated through relevant experiments.The results show that the method has a ground target detection accuracy mAP of 70.2%in typical camouflage scenarios,which is 5.7%higher than before improvement.It can effectively output the azimuth distance between the target and the UAV,and the average operating efficiency of the algorithm can reach 29.4 fps,which can meet the real-time requirement of UAV ground target detection and positioning.