Plate Number Recognition for International Islamic University Malaysia Gate Security using Machine Learning
DOI:
https://doi.org/10.15282/mekatronika.v7i1.12064Keywords:
Real-time, Number Plate Recognition, YOLOv5, EasyOCR, Computer Vision, Object DetectionAbstract
The increasing number of vehicles at the International Islamic University Malaysia (IIUM) has led to challenges in traffic and parking management, posing significant enforcement difficulties for the Office of Security Management (OSeM). Therefore, a system needs to be designed to assist the university's policy enforcement to impose these rules and help OSeM to check for unregistered vehicles. To address these issues, this study proposes a robust and efficient vehicle license plate recognition system based on the state-of-the-art YOLOv5 object detection model. The proposed model was trained on a custom Malaysian vehicle dataset comprising 1,026 images. The proposed model detects and localizes the license plate, extracts the region of interest, and performs character recognition using EasyOCR. The YOLOv5 model achieved a mean average precision (mAP) of 99.5% for mAP@0.5-0.95, 87.7% at mAP@0.5, and a processing speed of 30 frames per second. The findings indicate that optimizing image-capturing techniques can further enhance detection accuracy, contributing to a more reliable real-time license plate recognition system for effective policy enforcement.
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