Urban Target Detection Algorithm Based on Multi-Modal Fusion of UAV
WANG Jianyuan1， CHEN Xiaotong1， ZHANG Yue1， SUN Junge2，
SHI Donghao1， CHEN Jinbao1
1. College of Astronautics， Nanjing University of Aeronautics and Astronautics， Nanjing 211100， Jiangsu， China；
2. Shanghai Electro-Mechanical Engineering Institute， Shanghai 201109，China
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 0.3s, indicating the high real-time performance.
2024, Vol. 7 
