OPTIMIZING TRANSPORTATION SURVEILLANCE WITH YOLOV7: DETECTION AND CLASSIFICATION OF VEHICLE LICENSE PLATE COLORS
DOI:
https://doi.org/10.33480/jitk.v10i4.6260Keywords:
Classification, detection, license plate colour, transportation monitoring systemAbstract
Optimizing transportation surveillance requires accurate vehicle license plate color detection and classification; however, existing systems face significant challenges in achieving real-time accuracy and robustness, particularly in crowded traffic scenarios with varying lighting and plate conditions. In Indonesia, vehicle license plates are color-coded based on their usage, including white and black for private vehicles, yellow for public vehicles, red for government vehicles, and green for free-trade areas. Each plate color plays a crucial role in transportation management, enabling proper vehicle identification and regulation. Existing surveillance systems struggle with real-time detection accuracy, especially in distinguishing plate colors in crowded traffic. Traditional methods may not efficiently classify plate colors due to limitations in feature extraction and processing. To address this, this study implements the YOLOv7 model to improve vehicle license plate color detection (black, white, yellow, and red) while distinguishing non-plate vehicles in diverse scenarios. The model's effectiveness is evaluated using precision, recall, and F1-score to ensure robustness for surveillance applications. Results show an average precision of 95.27%, recall of 94.60%, and F1-score of 94.93%, demonstrating strong detection capabilities. Optimizing the Non-Plate category further improves system accuracy, efficiency, and scalability, enhancing transportation monitoring reliability.
Downloads
References
E. Dilek and M. Dener, “Computer Vision Applications in Intelligent Transportation Systems: A Survey,” Mar. 01, 2023, MDPI. doi: 10.3390/s23062938.
F. Xiao, H. Wang, Y. Li, Y. Cao, X. Lv, and G. Xu, “Object Detection and Recognition Techniques Based on Digital Image Processing and Traditional Machine Learning for Fruit and Vegetable Harvesting Robots: An Overview and Review,” Mar. 01, 2023, MDPI. doi: 10.3390/agronomy13030639.
M. A. Chung, Y. J. Lin, and C. W. Lin, “YOLO-SLD: An Attention Mechanism-Improved YOLO for License Plate Detection,” IEEE Access, vol. 12, pp. 89035–89045, 2024, doi: 10.1109/ACCESS.2024.3419587.
M. Hussain, “YOLO-v1 to YOLO-v8, the Rise of YOLO and Its Complementary Nature toward Digital Manufacturing and Industrial Defect Detection,” Jul. 01, 2023, Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/machines11070677.
A. Ammar, A. Koubaa, W. Boulila, B. Benjdira, and Y. Alhabashi, “A Multi-Stage Deep-Learning-Based Vehicle and License Plate Recognition System with Real-Time Edge Inference,” Sensors, vol. 23, no. 4, Feb. 2023, doi: 10.3390/s23042120.
peraturan.bpk.go.id, “Peraturan Kepolisian Negara Republik Indonesia Nomor 7 Tahun 2021 Registrasi dan Identifikasi Kendaraan Bermotor,” https://peraturan.bpk.go.id/Details/225016/perpol-no-7-tahun-2021.
peraturanpolri.com, “Peraturan Kapolri Nomor 05 tahun 2012 tentang Registrasi dan Identifikasi Kendaraan Bermotor,” https://www.peraturanpolri.com/2015/12/peraturan-kapolri-nomor-05-tahun-2012.html.
Kanwil DJKN Riau, “Penggantian Plat Nomor Polisi Hitam ke Plat Polisi Putih untuk kendaraan pribadi,” https://www.djkn.kemenkeu.go.id/kanwil-rsk/baca-artikel/15417/Penggantian-Plat-Nomor-Polisi-Hitam-ke-Plat-Polisi-Putih-untuk-kendaraan-pribadi.html.
D. Yu, Z. Yuan, X. Wu, Y. Wang, and X. Liu, “Real-Time Monitoring Method for Traffic Surveillance Scenarios Based on Enhanced YOLOv7,” Applied Sciences, vol. 14, no. 16, p. 7383, Aug. 2024, doi: 10.3390/app14167383.
C.-Y. Wang, A. Bochkovskiy, and H.-Y. M. Liao, “YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors,” in 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Jun. 2023, pp. 7464–7475. doi: 10.1109/CVPR52729.2023.00721.
M. N. Azmi, F. Bimantoro, and A. Y. Husodo, “Implementation of YOLOv7 (You Only Look Once v7) Method for Traffic Density Detection,” in ICADEIS 2023 : the International Conference on Advancement in Data Science, Lombok, Indonesia: IEEE, 2023, p. 298.
S. Pan, J. Liu, and D. Chen, “Research on License Plate Detection and Recognition System based on YOLOv7 and LPRNet,” 2022.
R. Sholehurrohman and B. Setiyono, “Improved YOLOv3 with fiture ekstraktor MobileNetv2 for detection and classification of moving vehicles,” 2021.
M. A. R. Alif and M. Hussain, “YOLOv1 to YOLOv10: A comprehensive review of YOLO variants and their application in the agricultural domain,” Jun. 2024, [Online]. Available: http://arxiv.org/abs/2406.10139
X. Xu, Y. Jiang, W. Chen, Y. Huang, Y. Zhang, and X. Sun, “DAMO-YOLO : A Report on Real-Time Object Detection Design,” Nov. 2022, [Online]. Available: http://arxiv.org/abs/2211.15444
M. L. Ali and Z. Zhang, “The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection,” Dec. 01, 2024, Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/computers13120336.
G. Chen, R. Cheng, X. Lin, W. Jiao, D. Bai, and H. Lin, “LMDFS: A Lightweight Model for Detecting Forest Fire Smoke in UAV Images Based on YOLOv7,” Remote Sens (Basel), vol. 15, no. 15, Aug. 2023, doi: 10.3390/rs15153790.
M. R. Sholahuddin, F. Atqiya, S. R. Wulan, M. Harika, S. Fitriani, and Y. Sofyan, “Implementasi Sistem Identifikasi Senjata Real Time Menggunakan YOLOv7 dan Notifikasi Chat Telegram,” Journal of Information System Research (JOSH), vol. 4, no. 2, pp. 598–606, Jan. 2023, doi: 10.47065/josh.v4i2.2774.
L. Liang, Y. Zhang, S. Zhang, J. Li, A. Plaza, and X. Kang, “Fast Hyperspectral Image Classification Combining Transformers and SimAM-Based CNNs,” IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1–19, 2023, doi: 10.1109/TGRS.2023.3309245.
S. Li, J. Yu, and H. Wang, “Damages Detection of Aeroengine Blades via Deep Learning Algorithms,” IEEE Trans Instrum Meas, vol. 72, pp. 1–11, 2023, doi: 10.1109/TIM.2023.3249247.
Y. Wang, Z.-P. Bian, Y. Zhou, and L.-P. Chau, “Rethinking and Designing a High-Performing Automatic License Plate Recognition Approach,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 7, pp. 8868–8880, Jul. 2022, doi: 10.1109/TITS.2021.3087158.
Y. Guo, S. Chen, R. Zhan, W. Wang, and J. Zhang, “LMSD-YOLO: A Lightweight YOLO Algorithm for Multi-Scale SAR Ship Detection,” Remote Sens (Basel), vol. 14, no. 19, p. 4801, Sep. 2022, doi: 10.3390/rs14194801.
X. Ding, X. Zhang, N. Ma, J. Han, G. Ding, and J. Sun, “RepVGG: Making VGG-style ConvNets Great Again,” Jan. 2021.
Z. Yang, R. Xie, L. Liu, and N. Li, “Dense-YOLOv7: improved real-time insulator detection framework based on YOLOv7,” International Journal of Low-Carbon Technologies, vol. 19, pp. 157–170, Jan. 2024, doi: 10.1093/ijlct/ctad122.
L. Yu, “Face Mask Detection Based on YSK Neural Network for Smart Campus,” 2023, pp. 575–584. doi: 10.1007/978-981-99-0848-6_46.
A. Thakuria and C. Erkinbaev, “Improving the network architecture of YOLOv7 to achieve real-time grading of canola based on kernel health,” Smart Agricultural Technology, vol. 5, p. 100300, Oct. 2023, doi: 10.1016/j.atech.2023.100300.
J. Chen, S. Bai, G. Wan, and Y. Li, “Research on YOLOv7-based defect detection method for automotive running lights,” Systems Science & Control Engineering, vol. 11, no. 1, Dec. 2023, doi: 10.1080/21642583.2023.2185916.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Ridho Sholehurrohman, Kurnia Muludi, Joko Triloka

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.