Research on Unmanned Aerial Vehicle Target Detection and Tracking System Based on Deep Learning
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[1] WANG Anping. Design and Implementation of UAV Management and Control Platform for Urban Governance[J/OL]. Software LI Zhipeng. Research on Relative Positioning Methods for UAVs Based on Multi-Sensor Fusion[D]. Nanjing University of Posts and Telecommunications,2024.DOI:10.27251/d.cnki.gnjdc.2024.000243.
[2] WU Ting. Object Tracking Using Improved YOLO Algorithm in UAV Environments[D]. Yangtze University,2023.DOI:10.26981/d.cnki.gjhsc.2023.001155.
[3] WEI Ruige. Research on Small Object Detection Algorithm for UAVs Based on Deep Learning[D]. University of Electronic Sci-ence and Technology of China,2022.DOI:10.27005/d.cnki.gdzku.2022.002921.
[4] Redmon J ,Divvala K S ,Girshick B R , et al. You Only Look Once: Uniffed, Real-Time Object Detection.[J].CoR-R,2015,abs/1506.02640
[5]Shaoqing R ,Kaiming H ,Ross G , et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks.[J].IEEE transactions on pattern analysis and machine intelligence,2017,39(6):1137-1149.DOI:10.1109/TPAMI.2016.2577031.
[6] You L ,Chen Y ,Xiao C , et al. Multi-Object Vehicle Detection and Tracking Algorithm Based on Improved YOLOv8 and ByteT-rack[J].Electronics,2024,13(15):3033-3033.DOI:10.3390/ELECTRONICS13153033
DOI: http://dx.doi.org/10.70711/cle.v2i10.9810
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