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.