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

Fengyun Huang; Xiaoxiong Wang; Yulin Zuo

doi:10.1117/12.2684947

19 October 2023 Recognition and positioning of vehicle nameplate in inspection scene based on binocular vision

Fengyun Huang, Xiaoxiong Wang, Yulin Zuo

Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 1270918 (2023) https://doi.org/10.1117/12.2684947
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China

Abstract

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.

Citation Download Citation

Fengyun Huang, Xiaoxiong Wang, and Yulin Zuo "Recognition and positioning of vehicle nameplate in inspection scene based on binocular vision", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 1270918 (19 October 2023); https://doi.org/10.1117/12.2684947

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KEYWORDS

Detection and tracking algorithms

Binocular vision

Target recognition

3D modeling

3D image reconstruction

Reconstruction algorithms

Target detection

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