To solve the problem that the existing license plate recognition algorithm process is complex and the real-time recognition detection is ineffective, a license plate recognition method based on the principle of color difference components and clustering is proposed. Firstly, the license plate image is denoised by median filter, then the chromatic aberration image is obtained by mathematical operation using the RGB channel characteristics of the image, then the optimal threshold value is searched by maximum entropy traversal method, the chromatic aberration image is segmented by threshold value, and the minimum bounding rectangle of the segmented area is marked, and the clustering principle is introduced to merge the disconnected areas of some special characters. Distance thresholds are derived from the supervised learning of the two classifications and are ultimately marked for subsequent feature extraction. The results of sensitivity and accuracy index calculation are 97.4% and 96.1%, respectively. The recognition accuracy is 96.6%, which basically meets the practical application.
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.
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