基于深度学习的智能化无人机视觉系统设计OACSTPCD

To address the problems challenges such as high flight altitude,large changes in target scale,and the densely occluded tar-gets,the study utilizes deep learning methods for target detection and identification and visual localization of UAV.A UAV target detec-tion and positioning system is designed and discussed in depth.The study selects Yolov5 deep learning network model for target detec-tion,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.