Traffic Sign Recognition System Using YOLOv8 Algorithm
DOI:
https://doi.org/10.58526/jsret.v4i4.946Keywords:
Object Detection, Autonomous Vehicles, Traffic Signs, YOLOv8Abstract
The advancement of artificial intelligence technologies has driven significant innovation in intelligent transportation systems, particularly in autonomous vehicles that require real-time object detection capabilities. This study develops a web-based traffic sign detection system using the YOLOv8 (You Only Look Once version 8) algorithm. The dataset consists of 5,224 annotated images representing 20 classes of traffic signs, collected through direct image acquisition and managed using Roboflow. The preprocessing stage includes resizing all images to 640×640 pixels and applying data augmentation to enhance model generalization. Model training was conducted on Google Colab using an optimal configuration of 50 epochs, a batch size of 32, and a learning rate of 0.001, resulting in a Precision score of 0.9406, a Recall of 0.9395, and an mAP50 of 0.9748. The trained model was integrated into a Flask-based web application to support image, video, and real-time camera detection. The results indicate that the system is capable of detecting traffic signs with high accuracy and strong computational efficiency.
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Akbar, M. (2022). Pengenalan Rambu Lalu-lintas menggunakan Convolutional Neural Network (Studi Kasus: Rambu Lalu-lintas Indonesia). 6(2). https://doi.org/10.30743/infotekjar.v6i2.4564
Arya Satya Pratama, D. (2024). Pengembangan Kontrol Adaptif untuk Kendaraan Otonom dengan Studi Kasus pada Mobil Elektrik Berbasis Deep Reinforcement Learning. 18–2024.
Ayala, R., & Mohd, T. K. (2021). Sensors in Autonomous Vehicles: A Survey. Journal of Autonomous Vehicles and Systems, 1(3). https://doi.org/10.1115/1.4052991
Dedy Kusuma, & Mauizah. (2023). Deteksi Lampu Lalu Lintas Menggunakan YOLOuntuk Autonomous Car. Journal of Computer Engineering, Network, and Intelligent Multimedia (JCENIM).
Fadilla Putri, R., Hidayatullah, Y. P., Aryansyah, A. D., & Feni, R. (2024). SOSIALISASI PENGENALAN RAMBU LALU LINTAS SERTA PENERAPAN HUKUM SEJAK DINI PADA SISWA SDN 26 KOTA BENGKULU. In Jurnal Kajian Ilmiah Interdisiplinier (Vol. 8, Issue 9).
IKBAL, MUH., & Saputra, R. A. (2024). PENGENALAN RAMBU LALU LINTAS MENGGUNAKAN METODE YOLOV8. JIKA (Jurnal Informatika), 8(2), 204. https://doi.org/10.31000/jika.v8i2.10609
Kresnanto, N. C., & Bahy, M. B. (2023). Method for Establishing a Spatial Database of Traffic Signs with Machine Learning. MEDIA KOMUNIKASI TEKNIK SIPIL, 29(1), 61–69. https://doi.org/10.14710/mkts.v29i1.49928
Liyanthy, M. (2024). Perancangan Data Pipeline Computer Vision untuk Mendukung Pelatihan Agen Otonom dalam Mengenali Rambu Lalu Lintas. In Journal of Computer and Information Systems Ampera (Vol. 5, Issue 1). https://journal-computing.org/index.php/journal-cisa/index
Mareeswari, V., Vijayan, R., Nisthar, S., & Krishnan, R. B. (2025). Traffic Sign Detection and Recognition Using Yolo Models. International Journal of Information Technology and Computer Science, 17(3), 13–25. https://doi.org/10.5815/ijitcs.2025.03.02
Mawaring Wening, P., Hendry, J., & Wicaksono Santoso, A. (2025). Traffic Signs Detection System Based on You Only Look Once (Yolov8) using Raspberry Pi. Jurnal Pekommas, 10(1). https://doi.org/10.56873/jpkm.v9i1.5832
Mita Nalsalisa Br Barus, Clarissa Oktoferin Sinaga, Vadillah Raihto Hutasuhut, & Syuratty Astuti Rahayu Manalu. (2024). Analisis Dampak Pelanggaran Lalu Lintas terhadap Keselamatan Masyarakat dan Upaya Mempromosikan Nilai-Nilai Kemanusiaan dan kesadaran berkendara. Jurnal Insan Pendidikan Dan Sosial Humaniora, 2(3), 150–160. https://doi.org/10.59581/jipsoshum-widyakarya.v2i3.3438
Oladri Renuka. (2024). A YOLOv8-based approach for multi-class traffic sign detection. International Journal of Science and Research Archive, 11(2), 824–829. https://doi.org/10.30574/ijsra.2024.11.2.0499
Sumit Shevtekar, & Shrinidhi kulkarni. (n.d.). Traffic-sign Recognition and Detection using Yolo-v8. https://doi.org/10.13140/RG.2.2.19007.11683
Taufiqurrahman, T., Hadi, A. P., & Siregar, R. E. (2024). Evaluasi Performa Yolov8 Dalam Deteksi Objek Di Depan Kendaraan Dengan Variasi Kondisi Lingkungan. Jurnal Minfo Polgan, 13(2), 1755–1773. https://doi.org/10.33395/jmp.v13i2.14228
Tessa Ningrum, A., Wijay, R., Rizal Abdul Aziz, M., Yudha Mauluda, M., & Rosyani, P. (2024). Face Deteksi Objek pada Gambar dan Video dengan YOLOv8 (Counting Objects). In Jurnal Artificial Inteligent dan Sistem Penunjang Keputusan (Vol. 2, Issue 2). https://jurnalmahasiswa.com/index.php/aidanspk
Wang, T. S., Kim, G. T., Shin, J., & Jang, S. W. (2024). Hierarchical Image Quality Improvement Based on Illumination, Resolution, and Noise Factors for Improving Object Detection. Electronics (Switzerland), 13(22). https://doi.org/10.3390/electronics13224438
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