YOLO V5 for Vehicle Plate Detection in DKI Jakarta

Abstract

The odd-even rule on vehicle number plates in DKI Jakarta aims to reduce congestion that occurs in DKI Jakarta. The application of these regulations is constrained by the limitations of the manual supervision function by officers. This problem can be overcome by implementing intelligence in the form of detecting number plate objects with the YOLO v5 algorithm and the character extraction process with Optical Character Recognition technology using Tesseract OCR. Object detection technology will detect objects in the form of vehicle plates. The OCR method can extract the characters on the number plate, the extraction results can be processed into parameter categorization so that the program can distinguish between vehicles that violate the rules and do not violate the rules automatically and more effectively and minimize errors. Based on this research, the average percentage of objects detected in each video is 92.38%, and the average confidence value obtained in object detection is between 75.55%. The success rate of the character extraction process on number plates is 95.45%, and the average proportion according to the detected number plate category is 97.2%. The implementation of the YOLO Algorithm has succeeded in detecting license plates with odd and even categories on videos that can provide signs and save violations of vehicles that violate the odd and even rules.