Tunnel speed limit monitoring device based on probability model and license plate recognition

Yan Yang; Juntao Hou

doi:10.1117/12.2634540

12 May 2022 Tunnel speed limit monitoring device based on probability model and license plate recognition

Yan Yang, Juntao Hou

Proceedings Volume 12173, International Conference on Optics and Machine Vision (ICOMV 2022); 121731H (2022) https://doi.org/10.1117/12.2634540
Event: International Conference on Optics and Machine Vision (ICOMV 2022), 2022, Guangzhou, China

Abstract

Aiming at the current situation that some drivers violate the regulations and overspeed when driving in multi-type tunnel sections, a tunnel speed limiting device based on probability model and license plate recognition is proposed to monitor the overspeed of vehicles in tunnel sections. Firstly, a camera is arranged at the entrance and exit of the tunnel to capture the license plate images of vehicles in and out. The license plate images are preprocessed by improved gray transformation, and the images are enhanced according to the tunnel structure and environmental characteristics. The Prewitt operator is used to detect the edge of the preprocessed images. The oTSU threshold segmentation algorithm is used to segment the target region from the image, and the probability model is established based on the parameters of gray co-occurrence matrix, and the feature region is screened to recognize the license plate. The time was recorded when the vehicle entered and left the tunnel, and the average speed was compared with the speed limit value to judge whether the vehicle was overspeed. MATLAB software is used to simulate the experimental process, and the test proves that the device can accurately judge the overspeed situation of vehicles in the tunnel, which is of certain significance to the tunnel speed limit monitoring.

Citation Download Citation

Yan Yang and Juntao Hou "Tunnel speed limit monitoring device based on probability model and license plate recognition", Proc. SPIE 12173, International Conference on Optics and Machine Vision (ICOMV 2022), 121731H (12 May 2022); https://doi.org/10.1117/12.2634540

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