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A Comparison of YOLO Models for License Plate Detection
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License plate detection and recognition (LPDR) has become an increasingly popular field of study under image recognition. The ability to autonomously create a bounding box around a license plate using machine learning models for image recognition is an important component of intelligent transportation systems. The YOLO model has become a popular CNN model for image detection because of its speed and accuracy. This work focuses on the use of the YOLO model for object localization as applied to license plate detection. The research compares the accuracy and speed between the YOLOv3 and YOLOv4 models along with various configuration parameters and finds that the YOLOv4 model is most accurate while the YOLOv3 with reduced image resolution is the fastest in prediction time.
Keywords
Image recognition, LPDR, YOLO Model.
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