To solve problems such as limited detection field of view, easy occlusion of target and weak image information of single light source in the current process of single unmanned aerial vehicle (UAV) target detection, and improve the reliable and efficient perception and computing capability of UAV, a multi-modal target detection method for multi-UAV cooperation is proposed in this article. Firstly, a multimodal object detection algorithm based on visible light and infrared fusion is studied, and a dual light fusion model based on a convolutional fusion network driven by visual tasks is proposed. The fused image is fed back to the fusion network through the semantic segmentation network, and a dual light image fusion model with a smaller loss is iteratively trained on the fusion network parameters. Then, the fused image is input into the visual perception enhancement module for image enhancement, eliminating the impact of poor lighting conditions on image quality and improving the preservation of target detail features. The effectiveness of the algorithm is verified on the MSRS dataset. In addition, an active perception process based on distributed biosensing processing is proposed for multi-drone collaborative detection. By calculating the detection confidence of the drone at the location of the sensed target and allocating the detection priority of the host and slave through the release of pheromones, a guidance strategy for multi-drone collaborative detection tasks is completed, achieving target detection in unstructured ground scenes under different lighting conditions. Experimental results show that the algorithm has a 56.55 ms delay and 45.84 fps reasoning speed on the unmanned aerial intelligent edge computing platform RK3588, and can accurately detect typical military targets deployed in ground scenes, with an average detection accuracy of 78.5%.