This project uses Yolov5 target detection algorithm and Jetsonnano to carry out uav target tracking, delivery and landing on Pixhawk UAV Hardware parameters：Jetson nano B01、Pixhawk2.4.8 Communication protocol：Mavlink The YOLOv5 training weights file is located in the./code/run/train folder Yolov5 target detection(detect1.py) and uav flight control(takeoff.py), Python multi-threaded programs(main.py) in the Code folder The Yolov5 output layer is modified and the target point coordinate system is improved to takeoff.py The final demonstration video of the drone is in the Experiment folder Email: 510677932@qq.com
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ZhuYaoHui1998/Deep-Learning-Based-Autonomous-Delivery-and-Landing-of-Drones
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This project uses Yolov5 target detection algorithm and Jetsonnano to carry out uav target tracking, delivery and landing on Pixhawk UAV
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