Sistem Pendeteksi Lampu Lalu Lintas untuk Kendaraan Pintar Menggunakan YOLOv9 Berbasis PyTorch
DOI:
https://doi.org/10.46880/jmika.Vol10No2.pp543-553Keywords:
Autonomous Vehicles, Traffic Lights, YOLOv9, Computer Vision, Inference TimeAbstract
The ability to accurately detect traffic lights in real time is a crucial component for the safety and navigation of autonomous vehicles. This study aims to develop and evaluate a traffic light detection system (red, yellow, green) using the YOLOv9 deep learning algorithm based on the PyTorch framework. This study employs a quantitative approach with an experimental method utilizing the S2TLD dataset as well as additional data covering various lighting and weather conditions. The model was evaluated using Precision, Recall, Mean Average Precision (mAP), and inference time metrics. Test results show that the YOLOv9 model is highly effective, achieving a 98.0% mAP@0.5 in standalone testing and 96.5% when tested directly on a dashboard system using a local dataset. Additionally, the system recorded 0% fatal cross-color classification errors; the system recorded 0% cross-color classification errors on the test dataset, demonstrating a high level of reliability to support smart vehicle systems. In terms of processing speed, the system achieves an average inference time of 41.1 ms per frame (±24 FPS) using GPU acceleration. In conclusion, the YOLOv9 architecture has proven to be accurate, safe, and robust for implementation as an autonomous navigation perception system.
References
Assagaf, I. P. A., & Prasetiyo, A. B. (2026). Kecerdasan Buatan dalam Kendaraan Otonom: Tinjauan Sistematis terhadap Tren dan Tantangan Masa Depan. Journal of Energy, Materials, & Manufacturing Technology, 5(01), 40-48. https://doi.org/10.61844/jemmtec.v5i01.1366
Bakirci, M. (2024). Real-Time Vehicle Detection Using YOLOv8-Nano for Intelligent Transportation Systems. Traitement Du Signal, 41(04), 1727–1740. https://doi.org/10.18280/ts.410407
Chen, D., Dai, L., Yue, X., Gu, Q., Huang, S., Pan, J., Zhang, Y., & He, M. (2025). AW-YOLO: A Multi-Object Detection Network for Autonomous Driving Under all Weather Conditions. IET Image Processing, 19(1). https://doi.org/10.1049/ipr2.70111
Du, S., Pan, W., Li, N., Dai, S., Xu, B., Liu, H., Xu, C., & Li, X. (2024). TSD-YOLO: Small traffic sign detection based on improved YOLO v8. IET Image Processing, 18(11), 2884–2898. https://doi.org/10.1049/ipr2.13141
Flores-Calero, M., Astudillo, C. A., Guevara, D., Maza, J., Lita, B. S., Defaz, B., Ante, J. S., Zabala-Blanco, D., & Armingol Moreno, J. M. (2024). Traffic Sign Detection and Recognition Using YOLO Object Detection Algorithm: A Systematic Review. In Mathematics (Vol. 12, Number 2). Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/math12020297
Golcarenarenji, G., Martinez-Alpiste, I., Wang, Q., & Alcaraz-Calero, J. M. (2024). Robust real-time traffic light detector on small-form platform for autonomous vehicles. Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, 28(5), 668–678. https://doi.org/10.1080/15472450.2023.2205018
Liao, J., Jiang, S., Chen, M., & Sun, C. (2025). SAM-YOLO: An Improved Small Object Detection Model for Vehicle Detection. The European Journal on Artificial Intelligence, 38(3), 279–295. https://doi.org/10.1177/30504554251319452
Öztürk, G., Eldoğan, O., & Köker, R. (2024). Computer Vision-Based Lane Detection and Detection of Vehicle, Traffic Sign, Pedestrian Using YOLOv5. Sakarya University Journal of Science, 28(2), 418–430. https://doi.org/10.16984/saufenbilder.1393307
Possatti, L. C., Guidolini, R., Cardoso, V. B., Berriel, R. F., Paixão, T. M., Badue, C., De Souza, A. F., & Oliveira-Santos, T. (2019). Traffic Light Recognition Using Deep Learning and Prior Maps for Autonomous Cars. https://doi.org/10.1109/IJCNN.2019.8851927
Wang, C.-Y., Yeh, I.-H., & Liao, H.-Y. M. (2024). YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information. http://arxiv.org/abs/2402.13616
Wu, T., & Fan, X. (2025). Nonlinear Filtering and YOLOv9-Based Moving Object Tracking Method Under Dynamic Scenarios. Journal of Circuits, Systems and Computers, 34(18), 2550203. https://doi.org/10.1142/S0218126625502032
Xie, Y., Du, D., & Liu, Y. (2025). RE-YOLO: Vehicle Detector Based on Knowledge Distillation YOLOv8 for Autonomous Driving Scenarios. Journal of Computing in Civil Engineering, 39(6), 4025084. https://doi.org/10.1061/JCCEE5.CPENG-6164
Zhou, Q., Zhang, D., Liu, H., & He, Y. (2024). KCS-YOLO: An Improved Algorithm for Traffic Light Detection under Low Visibility Conditions. Machines 2024, Vol. 12, Page 557, 12(8), 557. https://doi.org/10.3390/MACHINES12080557
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