Recognition and positioning of vehicle nameplate in inspection scene based on binocular vision

In the motor vehicle inspection scenario, accurate identification and positioning of vehicle nameplates is an important prerequisite for automated robotic inspection operations. In view of the current problems that vehicle nameplates are mainly checked manually and the level of intelligence is low, a target recognition and localisation method based on binocular vision combined with improved SURF algorithm is proposed. Firstly, feature points are extracted based on the SURF algorithm, and target recognition is achieved by combining the nearest neighbour ratio method with the RANSAC algorithm based on the gradient constraint of the parallax; then the edge contour of the target in the template image is obtained based on the Canny edge detection method, and the target shape centre on the template image is obtained based on the contour, and the left and right scene map target shape centres are obtained using the single-response matrix mapping; finally, target localisation is achieved by combining the principle of binocular vision 3D reconstruction. The experimental results show that the method has good recognition efficiency and accuracy, and can successfully identify and locate the target, which has certain application value.