基于深度学习的智能化无人机视觉系统设计  

Design of intelligent UAV vision system based on deep learning technology

作　　者：陈忠财 李鑫 隋立林 CHEN Zhongcai;LI Xin;SUI Lilin(Guoneng Xinjiang Toksun Energy Co.,Ltd.,Toksun 838100,China;CHN Energy Digital Intelligence Technology Development(Beijing)Co.,Ltd.,Beijing 100011,China)

机构地区：[1]国能新疆托克逊能源有限责任公司,托克逊838100 [2]国能数智科技开发(北京)有限公司,北京100011

出　　处：《集成电路与嵌入式系统》2024年第9期62-67,共6页Integrated Circuits and Embedded Systems

摘　　要：针对无人机飞行高度高、目标尺度变化大、目标存在密集遮挡等问题,本文将对基于深度学习的无人机目标探测与识别以及视觉定位进行深入探讨,并设计出一套无人机目标探测与定位系统。研究选用Yolov5深度学习网络模型进行目标检测,基于Darknet深度学习框架完成端到端的训练预测,最后利用AprilTag视觉基准库完成无人机自身空间位置的辅助定位。测试结果表明,Yolov5模型的参数量只有5.3,准确率为97.41%,召回率为90.73%,mAP为83.2。AprilTag辅助定位的拟合精度达95%以上。研究设计的基于深度学习的智能化无人机视觉系统不仅具有实际的工程价值,更具有重要的社会意义。To address the problems challenges such as high flight altitude,large changes in target scale,and the densely occluded targets,the study utilizes deep learning methods for target detection and identification and visual localization of UAV.A UAV target detection and positioning system is designed and discussed in depth.The study selects Yolov5 deep learning network model for target detection,completes end-to-end training prediction based on Darknet deep learning framework,and finally utilizes AprilTag visual benchmark library to complete the auxiliary localization of UAV's spatial positioning.The test results show that the parameter count of Yolov5 model is only 5.3.Meanwhile,the model achieves a precision of 97.41%,a recall rate of 90.73%,and an mAP of 83.2.The fitting precision of AprilTag assisted localization is more than 95%.The research and design of intelligent UAV vision system based on deep learning not only has actual engineering values,but also significant societal importance.

关 键 词：无人机 深度学习 目标检测 视觉定位 Yolov5 

分 类 号：TN91[电子电信—通信与信息系统]