Under low light conditions, factors such as low image brightness, weak contrast, poor imaging quality and the constraints of on-board arithmetic greatly affect the detection accuracy from the UAV′s point of view. Therefore, researches based on object detection under low light conditions in UAVs is of great significance. Aiming at this problem, this paper proposes a multiscale differential attention fusion detection method with coupled illumination conditions and contrast. First, an information-aware module is designed to guide the multiscale differential attention module. This module deeply fuses the intra- and inter-modal features of visible and infrared images through calculating the light information and local contrast, thereby enhancing the recognition ability under low light conditions. Second, a rotary-wing UAV multimodal target detection system is constructed based on multimodal pods, edge computing modules and selforganizing network radios. This system has a standardized transmission protocol and a unified task management mechanism for communication interaction and realizes synchronous decoding. Subsequently, comparison and ablation experiments are designed, and the results show that the mAP of this method on the LLVIP is 69. 2% , which is 3. 9% better than before the improvement, and outperforms LRAF-Net. Finally, the proposed algorithm is validated at the airborne end of USVs, demonstrating that it can significantly improve the detection capability of UAVs on targets under low light conditions. The average operation efficiency can reach 21. 2 FPS, which meets the requirements of airborne applications.