空天防御2024，Vol.7Issue(1)：32-39,8.
无人机多模态融合的城市目标检测算法
Urban Target Detection Algorithm Based on Multi-Modal Fusion of UAV
摘要
Abstract
Using small drones to detect urban targets such as vehicles at low altitudes in cities has gradually become a mainstream means.Given the existing problems of low detection accuracy of single-mode detection networks affected by visible light detection,inability to work at night and the blurred edge of infrared detection targets in actual scenes,this paper has proposed a multi-modal UAV detection algorithm based on image fusion and deep learning network.Firstly,based on the DUT-VTUAV visible-infrared registration data set and TIF image fusion algorithm,a multi-mode fusion data set was built up.Secondly,by comparing the detection accuracy,speed and several parameters of the existing YOLO series network,the lightweight network YOLO v5n which was most suitable for the mobile deployment of UAVs was decided.Finally,a multi-modal fusion detection algorithm was produced by combining an image fusion algorithm and a target detection model.Comparative experiments on vehicle data sets successfully show that compared with single-mode detection,the detection accuracy of the proposed algorithm is effectively increased,with mAP up to 99.6%,and a set of visible-infrared image fusion detection can be completed within 0.3 s,indicating the high real-time performance.关键词
目标检测/YOLO检测/多模态融合/数据融合/TIF算法Key words
target detection/YOLO detection/multimodal fusion/data fusion/TIF algorithm分类
信息技术与安全科学引用本文复制引用
王建园,陈小彤,张越,孙俊格,石东浩,陈金宝..无人机多模态融合的城市目标检测算法[J].空天防御,2024,7(1):32-39,8.基金项目
国家自然科学基金企业创新发展联合基金集成项目(U21B6002) （U21B6002）
