Indonesian License Plate Detection and Recognition System using Gaussian YOLOv7

Indonesian License Plate Detection and Recognition System using Gaussian YOLOv7

detection, license plate, recognition, rectification, YOLO

In recent years, Automatic License Plate Recognition (ALPR) systems have garnered attention in computer vision research. However, practical applications face challenges such as inconsistent lighting diverse license plate designs, and environmental variations, which increase the complexity of the task and lead to more false detections. To address these issues, we proposed Gaussian YOLOv7 for license plate detection and character recognition within ALPR systems, along with the Spatial Transformer Network (STN) for rectifying license plate orientation, aiming to enhance performance and adaptability
to real-world scenarios. Additionally, we introduced a novel dataset for Indonesian ALPR systems to
ensure robust detection and a balanced class distribution. Evaluation results indicate that Gaussian YOLOv7 improves precision and reduces false positives by 37.5% in the detection stage, albeit with poorer performance in other metrics. Conversely, the implementation of STN results in decreased character recognition accuracy, underscoring its limited effectiveness. Despite these challenges, Gaussian YOLOv7 excels in license plate rectification, achieving a recall of 83.8% and reducing false positives by 50.13% compared to YOLOv7. Moreover, post-processing techniques introduced by our approach further enhance precision by 5.3% and recall by 1%. Overall, our approach offers promising
advancements in Indonesian ALPR systems, addressing fundamental challenges and enhancing
performance.

Juan Thomas Wijaya and Aniati Murni Arymurthy

DOI: http://dx.doi.org/10.21609/jiki.v18i2.1320

Faculty of Computer Science UI

2025-06-26

Sri Wahyuni

ISSN : 25029274

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