Performance Evaluation of YOLOv8 for Vehicle License Plate Detection Using Standard Object Detection Metrics
DOI:
https://doi.org/10.55537/bigint.v4i1.1527Keywords:
Google Colab, License Plate Detection, Object Detection, Performance Evaluation, YOLOv8Abstract
Vehicle license plate detection is a crucial computer vision task for traffic monitoring, automated parking, and vehicle identification. This study evaluates the performance of a YOLO-based license plate detection system implemented in Python and executed on Google Colab to ensure reproducibility. A public dataset of vehicle images with variations in lighting conditions and viewing angles is used for testing. Performance is assessed using precision, recall, F1-score, [email protected], and [email protected]:0.95. The results show a precision of 0.7653 and a recall of 0.6809, yielding an F1-score of 0.7206. The [email protected] reaches 0.7776, while the [email protected]:0.95 drops to 0.3572. As a contribution, this work provides a simple and replicable baseline evaluation workflow for YOLO-based license plate detection using standard object-detection metrics. The large gap between [email protected] and [email protected]:0.95 indicates that the model often detects the presence of license plates but struggles to localize them precisely under stricter IoU thresholds, highlighting localization sensitivity for small objects under real-world variations. These findings can guide future improvements through dataset diversification, augmentation, and higher-resolution training to enhance bounding box accuracy.
Downloads
References
L. Satya, M. R. D. Septian, M. W. Sarjono, M. Cahyanti, and E. R. Swedia, “Sistem pendeteksi plat nomor polisi kendaraan dengan arsitektur YOLOv8,” Sebatik, vol. 27, no. 2, pp. 753–761, Dec. 2023. https://doi.org/10.46984/sebatik.v27i2.2374
L. Alashrafi, R. Badawood, H. Almagrabi, M. Alrige, F. Alharbi, and O. Almatrafi, “Benchmarking lightweight YOLO object detectors for real-time hygiene compliance monitoring,” Sensors, vol. 25, no. 19, Oct. 2025. https://doi.org/10.3390/s25196140
N. H. Harani, C. Prianto, and M. Hasanah, “Deteksi objek dan pengenalan karakter plat nomor kendaraan Indonesia menggunakan metode convolutional neural network (CNN) berbasis Python,” Jurnal Teknik Informatika, vol. 11, no. 3, pp. 47–53, Aug. 2019. https://doi.org/10.23960/jitet.v11i1.2897
P. Jiang, D. Ergu, F. Liu, Y. Cai, and B. Ma, “A review of YOLO algorithm developments,” Procedia Computer Science, vol. 199, pp. 1066–1073, 2022. https://doi.org/10.1016/j.procs.2022.01.135
M. R. Joyonegoro and E. Setyati, “Deteksi dan pengenalan dua variasi plat nomor kendaraan bermotor di Indonesia dengan variasi waktu dan pencahayaan memanfaatkan YOLOv8 dan CNN,” KONVERGENSI, vol. 20, no. 1, pp. 11–17, Jan. 2024. https://doi.org/10.24912/jiksi.v10i1.17852
R. Sapkota and M. Karkee, “Ultralytics YOLO evolution: An overview of YOLO26, YOLO11, YOLOv8, and YOLOv5 object detectors for computer vision and pattern recognition,” arXiv preprint, Oct. 2025. https://doi.org/10.48550/arXiv.2510.09653
N. A. Kurniawan and C. A. Sari, “Automatic license plate detection system with YOLOv11 algorithm,” Journal of Applied Informatics and Computing (JAIC), vol. 9, no. 6, p. 3097, Dec. 2025. https://doi.org/10.30871/jaic.v9i6.11484
H. Moussaoui et al., “Enhancing automated vehicle identification by integrating YOLOv8 and OCR techniques for high-precision license plate detection and recognition,” Scientific Reports, vol. 14, no. 1, Dec. 2024. https://doi.org/10.1038/s41598-024-65272-1
R. Laroca et al., “A robust real-time automatic license plate recognition based on the YOLO detector,” in Proc. Int. Joint Conf. Neural Networks (IJCNN), Apr. 2018. https://doi.org/10.1109/IJCNN.2018.8489629
F. Martadinata, A. Firdaus, and M. R. T. Amal, “Detection and identification of vehicle license plates in Indonesia transportation system based on deep learning using YOLOv11 and EasyOCR,” Jurnal Sisfokom (Sistem Informasi dan Komputer), vol. 14, no. 4, pp. 573–578, Oct. 2025. https://doi.org/10.32736/sisfokom.v14i4.2524
N. B. S. Makkar, “VajraV1—The most accurate real-time object detector of the YOLO family,” arXiv preprint, Dec. 2025. https://doi.org/10.48550/arXiv.2512.13834
M. S. Zandi and R. Rajabi, “Deep learning based framework for Iranian license plate detection and recognition,” Multimedia Tools and Applications, Jan. 2022. https://doi.org/10.1007/s11042-022-12023-x
R. Sapkota, Z. Meng, M. Churuvija, X. Du, Z. Ma, and M. Karkee, “Comprehensive performance evaluation of YOLOv12, YOLO11, YOLOv10, YOLOv9, and YOLOv8 on detecting and counting fruitlet in complex orchard environments,” arXiv preprint, Feb. 2025. https://doi.org/10.48550/arXiv.2407.12040
S. Gholinavaz, N. Saeedi, and S. S. Gharehveran, “Robustness analysis of YOLO and Faster R-CNN for object detection in realistic weather scenarios with noise augmentation,” Scientific Reports, vol. 15, no. 1, Dec. 2025. https://doi.org/10.1038/s41598-025-28737-5
N. Jegham, C. Y. Koh, M. Abdelatti, and A. Hendawi, “YOLO evolution: A comprehensive benchmark and architectural review of YOLOv12, YOLO11, and their previous versions,” arXiv preprint, Mar. 2025. https://doi.org/10.48550/arXiv.2411.00201
V. N. Ribeiro and N. S. T. Hirata, “Efficient video-based ALPR system using YOLO and visual rhythm,” arXiv preprint, Jan. 2025. https://doi.org/10.48550/arXiv.2501.02270
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Kardandi Alfarizi Siregar, Bhagaskara Cahyadi, Legiman Samosir, Supiyandi Supiyandi
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
