Stereo visual perception system based on unmanned wheat harvester
In response to the problems of wheat harvesters being unable to achieve real-time obstacle avoidance of dynamic obstacles during unmanned operation on farms,and the low safety of unmanned driving technology,this paper designs an unmanned stereo vision perception system based on a combination of stereo vision and deep learning.The system first uses a stereo vision camera to collect grayscale images of left and right eyes,and calculates the distance between obstacles through the disparity of pixel positions in the image and the principle of stereo vision imaging;Then,the RGB images collected by the camera are processed through deep learning to achieve obstacle detection and recognition,ultimately completing the perception of dynamic obstacles.The research results indicate that the autonomous driving perception system based on stereo vision and deep learning has a detection rate of 30.1 fps and an accuracy rate of 98.24%for dynamic obstacles in unmanned driving operations on farms.The method proposed in this article can effectively meet the recognition requirements of dynamic obstacle detection during operation,significantly improving the safety and reliability of unmanned wheat harvesters during operation,and laying a theoretical and technical foundation for the development of intelligent unmanned agricultural machinery.
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