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

作　　者：Yan Ding Qingxin Cao Bozhi Zhang Peilin Li Zhongjiao Shi 

机构地区：[1]School of Aerospace Engineering,Beijing Institute of Technology,Beijing 100081,China

出　　处：《Defence Technology(防务技术)》2025年第4期213-226,共14页Defence Technology

基　　金：supported by the Natural Science Foundation of China,Grant No.62103052.

摘　　要：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.

关 键 词：Drone swarm systems Reconnaissance and strike Image generation Multi-view detection Pix2Pix framework Attention mechanism 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]