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Using deep learning in an embedded system for real-time target detection based on images from an unmanned aerial vehicle: vehicle detection as a case study

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International Journal of Digital Earth 2023年第1期16卷 910-936页

作者： Fang Huang Shengyi Chen Qi Wang Yingjie Chen Dandan Zhang School of Recourses and Environment University of Electronic Science and Technology of China(UESTC)ChengduPeople’s Republic of China Aerospace Information Research Institute(AIR) Chinese Academy of Sciences(CAS)BeijingPeople’s Republic of China School of Electronic Electrical and Communication EngineeringUniversity of Chinese Academy of SciencesBeijingPeople’s Republic of China

For a majority of remote sensing applications of unmanned aerial vehicles(UAVs),the data need to be downloaded to ground devices for processing,but this procedure cannot satisfy the demands of real-time target *** objective in this study is to develop a real-time system based on an embedded technology for image acquisition,target detection,the transmission and display of the results,and user interaction while providing support for the interactions between multiple UAVs and *** work is divided into three parts:(1)We design the technical procedure and the framework for the implementation of a real-time target detection system according to application requirements.(2)We develop an efficient and reliable data transmission module to realize real-time cross-platform communication between airborne embedded devices and ground-side servers.(3)We optimize the yolov4 algorithm by using the K-Means algorithm and TensorRT inference to improve the accuracy and speed of the NVIDIA Jetson *** experiments involving static detection,it had an overall confidence of 89.6%and a rate of missed detection of 3.8%;in experiments involving dynamic detection,it had an overall confidence and a rate of missed detection of 88.2%and 4.6%,respectively.

关键词： Unmanned aerial vehicle(UAV) embedded system deep learning yolov4 algorithm data transmission vehicle detection

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