Research on multi-view collaborative detection system for UAV swarms based on Pix2Pix framework and BAM attention mechanism

Drone swarm systems, equipped with photoelectric imaging and intelligent target perception, are essential for reconnaissance and strike missions in complex and high-risk environments. They excel in information sharing, anti-jamming capabilities, and combat performance, making them critical for future warfare. However, varied perspectives in collaborative combat scenarios pose challenges to object detection, hindering traditional detection algorithms and reducing accuracy. Limited angle-prior data and sparse samples further complicate detection. This paper presents the Multi-View Collaborative Detection System, which tackles the challenges of multi-view object detection in collaborative combat scenarios. The system is designed to enhance multi-view image generation and detection algorithms, thereby improving the accuracy and efficiency of object detection across varying perspectives. First, an observation model for three-dimensional targets through line-of-sight angle transformation is constructed, and a multi-view image generation algorithm based on the Pix2Pix network is designed. For object detection, YOLOX is utilized, and a deep feature extraction network, BA-RepCSPDarknet, is developed to address challenges related to small target scale and feature extraction challenges. Additionally, a feature fusion network NS-PAFPN is developed to mitigate the issue of deep feature map information loss in UAV images. A visual attention module (BAM) is employed to manage appearance differences under varying angles, while a feature mapping module (DFM) prevents fine-grained feature loss. These advancements lead to the development of BA-YOLOX, a multi-view object detection network model suitable for drone platforms, enhancing accuracy and effectively targeting small objects.