Aiming at the problems of low image recognition accuracy and long processing time in traditional autonomous obstacle avoidance vehicles under complex road conditions,a smart obstacle avoidance vehicle based on EdgeBoard is designed.Firstly,collect road images through a USB camera;Then,the road tracking algorithm based on OpenCV is used to identify the edges of the road;Finally,use the SSD_MobileNetV1 deep learning model to identify specific traffic signs.After testing,the intelligent tracking and obstacle avoidance car has good stability and reliability.
tracking and obstacle avoidance vehicleEdgeBoardOpenCVdeep learningautonomous tracking and obstacle avoidance
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