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Vehicle Number Plate Detection and Recognition System in Nigeria Using Deep Learning

P. C. Opara, Daniel Matthias, N. D. Nwiabu

Abstract


With over 200 million citizens, Nigeria is one of Africa’s largest and most populous nations. It is now essential to automate traffic management due to the rising number of vehicles in the nation. With the use of a deep learning technique known as the YOLO method, the study was able to construct an automatic vehicle license plate identification system that will recognize each of the vehicle license plates when many vehicles are present in a particular image or frame. In light of the foregoing, we employed fruitful research approach to accomplish the intended purpose of this study. According to the method, a digital camera is used to capture an input image of a system that contains automobiles. The required license plate letters are then moved on the Graphics User Interface, and the full process is then carried out and simulated in MATLAB. The license plate is localized when the vehicle has been identified. The most advanced YOLO (You Only Look Once) object detector was used for localization and detection. The Python programming language was employed to build the system, and XGBoost was used as the classifier to train and test the extracted features. A total of 110 vehicle photos were employed for license plate localization. To assess the proposed method’s performance against the current approach, two performance metrics, Peak Signal-to-Noise Ratio and Success Rate (%), were utilized based on the trial results. The accuracy of detecting vehicles overall was 97.5%, while the accuracy of locating license plates was 96.9%. The performance of the segmentation approach was examined using the recovered license plates from the localization step, and the segmentation accuracy was 95.1%. From the results, we were able to accurately extract each license plate’s characters from the single image with 96.5% success.


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References


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