Adaptive Bounding Box Coordinate Adjustment on License Plate Character Detection Using Machine Learning
DOI:
https://doi.org/10.21831/jraee.v2i1.553Keywords:
ANPR, Machine Learning, CNN, Object Detection, License PlateAbstract
Effective law enforcement, including the use of the ANPR (Automatic Number Plate Recognition) system, is essential for reducing the number of road traffic accidents. ANPR involves plate localization, character segmentation, and recognition to build a minimum system. This study aims to improve a character segmentation method using a detection approach to address issues like noisy or modified plates. We propose an adaptive improvement on an established sliding window technique, by integrating a CNN (Convolutional Neural Network) for bounding box coordinate adjustment to handle various plate conditions. The proposed method was tested on 280 license plate images and improved the average IoU (Intersection over Union) from 0.4811 to 0.8980. Hence, the recall and precision of the model could be improved to increase any character recognition performance.
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Badan Pusat Statistik, “Jumlah Kecelakaan, Korban Mati, Luka Berat, Luka Ringan, dan Kerugian Materi, 2022.” Accessed: May 15, 2024. [Online]. Available: https://www.bps.go.id/id/statistics- table/2/NTEzIzI=/jumlah-kecelakaan--korban-mati--luka-berat--luka-ringan--dan-kerugian-materi.html
M. M. Rezapour Mashhadi, P. Saha, and K. Ksaibati, “Impact of traffic Enforcement on Traffic Safety,” International Journal of Police Science & Management, vol. 19, no. 4, pp. 238–246, 2017, doi: 10.1177/1461355717730836.
Hendry and R. C. Chen, “A New Method for License Plate Character Detection and Recognition,” in ACM International Conference Proceeding Series 2018, pp. 204–208, doi: 10.1145/3301551.3301592.
C. H. Lin and Y. Li, “A License Plate Recognition System for Severe Tilt Angles Using Mask R-CNN,” in International Conference on Advanced Mechatronic Systems, ICAMechS, vol. 2019-Augus, pp. 229– 234, doi: 10.1109/ICAMechS.2019.8861691.
A. Abdussalam, S. Sun, M. Fu, H. Sun, and I. Khan, “License Plate Segmentation Method Using Deep Learning Techniques Amr,” Springer Singapore, 2018. [Online]. Available: https://doi.org/10.1007/978- 981-13-1733-0_8
I. W. Notonogoro, Jondri, and A. Arifianto, “Indonesian License Plate Recognition Using Convolutional Neural Network,” in 2018 6th International Conference on Information and Communication Technology, ICoICT, doi: 10.1109/ICoICT.2018.8528761.
R. Laroca et al., “A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector,” Proceedings of the International Joint Conference on Neural Networks, vol. 2018-July, 2018, doi: 10.48550/arXiv.1802.09567.
S. Abdullah, M. Mahedi Hasan, and S. Muhammad Saiful Islam, “YOLO-Based Three-Stage Network for Bangla License Plate Recognition in Dhaka Metropolitan City,” in 2018 International Conference on Bangla Speech and Language Processing, ICBSLP 2018, pp. 1–6, doi: 10.1109/ICBSLP.2018.8554668.
A. T. Musaddid, A. Bejo, and R. Hidayat, “Improvement of Character Segmentation for Indonesian License Plate Recognition Algorithm using CNN,” in 2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) 2020, pp. 279–283, doi: 10.1109/ISRITI48646.2019.9034614.
K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” in 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, pp. 1–14, doi: 10.48550/arXiv.1409.1556.
G. R. Gonçalves, S. P. G. da Silva, D. Menotti, and W. R. Schwartz, “Benchmark for License Plate Character Segmentation,” J Electron Imaging, vol. 25, no. 5, p. 053034, Oct. 2016, doi: 10.48550/arXiv.1607.02937.
