Performance Evaluation of YOLOv8 for Railway Switching Operation Safety Monitoring
DOI:
https://doi.org/10.31294/co-science.v6i1.11674Keywords:
safety, shunting, railway, YOLOv8, real-timeAbstract
Safety in railway shunting operations requires continuous monitoring of train distance and speed to reduce the risk of operational accidents. In practice, shunting activities are still highly dependent on manual observation and verbal communication, while the performance of vision based safety systems under real operational conditions remains uncertain. In addition, comprehensive performance evaluations of deep learning based object detection models in real shunting environments, particularly under different hardware capabilities and lighting conditions, are still limited. This study aims to evaluate the performance of the YOLOv8 algorithm for real-time distance and speed monitoring during railway shunting operations. The system was tested using a camera-based detection approach under different processor configurations, namely an internal CPU and an RTX GPU, and under morning, daytime, and nighttime lighting conditions. System performance was evaluated based on accuracy, precision, and real-time detection capability across these conditions. The results show that the system achieved an average accuracy of 87.32% when operating on a CPU which increased to 91.30% when using a GPU. Optimal performance was observed under adequate daylight conditions, while reduced lighting led to a decline in performance, particularly on CPU-based processing. These findings indicate that hardware configuration and lighting conditions play a critical role in determining the reliability of YOLOv8-based safety monitoring systems for railway shunting operations.
Downloads
References
Aditiatmoko, A., & Latifah, K. (2022). Assesmen Digital Untuk Menentukan Status Kelayakan Awak Kereta Api. Science And Engineering National Seminar 7 (SENS 7), 7, 1–7. https://conference.upgris.ac.id/index.php/sens/article/view/3611
Arifianto, T., Pradjojowaty, I. S., Feryando, D. A., Arifidin, M. A. A., Pratiwi, D. I., & Prasetijo, J. (2024). Wisata Edukasi Pengenalan Petugas Pelayanan Penumpang di Atas Kereta Api pada Siswa RA Babussalam Madiun. JURPIKAT (Jurnal Pengabdian Kepada Masyarakat), 5(1), 170–180. https://doi.org/10.37339/jurpikat.v5i1.1500
Asri, A. F., Bachtiar, V. S., Lestari, R. A., & Ridwan, R. (2025). Analisis dan Rekayasa Kebisingan Akibat Aktivitas Alat Transportasi Kereta Api Pada Permukiman (Studi Kasus: Jalur Kereta Api Stasiun Alai-Air Tawar). Dampak: Jurnal Teknik Lingkungan, 22(1), 19–35. https://doi.org/10.25077/dampak.22.1.19-35.2025
Brintha, K., & Jawhar, S. J. (2024). FOD-YOLO NET: Fasteners Fault and Object Detection in Railway Tracks using Deep YOLO Network. Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology, 46(4), 8123–8137. https://doi.org/10.3233/JIFS-236445
Dwiatmoko, H. (2025). Keselamatan Perkeretaapian. Perkumpulan Masyarakat Perkeretaapian Indonesia (Maska).
Guan, L., Jia, L., Xie, Z., & Yin, C. (2022). A Lightweight Framework for Obstacle Detection in the Railway Image Based on Fast Region Proposal and Improved YOLO-Tiny Network. IEEE Transactions on Instrumentation and Measurement, 71, 1–16. https://doi.org/10.1109/TIM.2022.3150584
Handoko, H. (2023). Sistem dan Manajemen Stasiun. CV. Mega Press Nusantara.
Kapoor, R., Goel, R., & Sharma, A. (2022). An Efficient Object and Railway Track Recognition in Thermal Images Using Deep Learning. Multimedia Tools and Applications, 841, 21083–21109. https://doi.org/10.1007/978-981-16-8774-7_20
Kim, T.-Y., Kim, C.-N., Park, C.-W., & Yum, B.-S. (2025). Human Error Accident Analysis of Railway Transport Workers in Shunting Operations : Application of the HEAR Methodology. Journal of Korean Society of Occupational and Environmental Hygiene, 35(3), 292–302. https://doi.org/10.15269/JKSOEH.2025.35.3.292
Lema, D. G., Usamentiaga, R., & García, D. F. (2024). Quantitative Comparison and Performance Evaluation of Deep Learning-Based Object Detection Models on Edge Computing Devices. Integration, 95, 102127. https://doi.org/10.1016/j.vlsi.2023.102127
Marsusiadi, E. N. S., Wiarco, Y., Wijayanti, L. M., & Iswanto, A. P. (2023). Kajian Penggunaan Bahasa Baku dalam Mewujudkan Komunikasi Efektif Awak Sarana Prasarana Melayani Perjalanan Kereta Api. EDUKASIA: Jurnal Pendidikan Dan Pembelajaran, 4(2), 1331–1346. https://doi.org/10.62775/edukasia.v4i2.439
Mauri, A., Khemmar, R., Decoux, B., Haddad, M., & Boutteau, R. (2022). Lightweight Convolutional Neural Network for Real-Time 3D Object Detection in Road and Railway Environments. Journal of Real-Time Image Processing, 19, 499–516. https://doi.org/10.1007/s11554-022-01202-6
Meng, C., Wang, Z., Shi, L., Gao, Y., Tao, Y., & Wei, L. (2023). SDRC-YOLO: A Novel Foreign Object Intrusion Detection Algorithm in Railway Scenarios. Electronics, 12(5), 1256. https://doi.org/10.3390/electronics12051256
Pfaff, R. (2023). Braking Distance Prediction for Vehicle Consist in Low-Speed On-Sight Operation: A Monte Carlo Approach. Railway Engineering Science, 31(2), 135–144. https://doi.org/10.1007/s40534-023-00303-7
Reichmann, M., Stephan, G., Wagner, A., Zajicek, J., Stadlmann, B., Wancura, H., Furian, N., & Vössner, S. (2025). Introducing the Concept of Grades of Automation for Shunting Operations. Journal of Rail Transport Planning & Management, 33, 100500. https://doi.org/10.1016/j.jrtpm.2024.100500
Sangadi, F. J., & Ratrikaningtyas, P. D. (2024). Analisis Fungsi Pendengaran Pada Masinis PT Kereta Api Indonesia (Persero): Kajian Tingkat Kebisingan Lokomotif. Jurnal Semesta Sehat, 4(2), 58–66. https://doi.org/10.58185/j-mestahat.v4i2.133
Sumarahardhi, P. C., & Santoso, D. B. (2023). Identifikasi dan Pemetaan Gangguan Komponen Sistem Persinyalan PT Kereta Api Indonesia (Persero) Resort Karawang. JATI (Jurnal Mahasiswa Teknik Informatika), 7(3), 1574–1578. https://doi.org/10.36040/jati.v7i3.6875
Wibisono, R. E., & Zidan, M. Y. (2023). Identifikasi Perawatan dan Pemeliharaan Wesel pada Wesel 209 di Stasiun Surabaya Gubeng. Mitrans: Jurnal Media Publikasi Terapan Transportasi, 1(1), 11–18. https://doi.org/10.26740/mitrans.v1n1.p11-18
Wu, Y., Qin, Y., Qian, Y., & Guo, F. (2021). Automatic Detection of Arbitrarily Oriented Fastener Defect in High-Speed Railway. Automation in Construction, 131, 1–15. https://doi.org/10.1016/j.autcon.2021.103913
Zhang, B., Yang, Q., Chen, F., & Gao, D. (2024). A Real-Time Foreign Object Detection Method Based on Deep Learning in Complex Open Railway Environments. Journal of Real-Time Image Processing, 21(166). https://doi.org/10.1007/s11554-024-01548-z
Zhao, X., Wang, L., Zhang, Y., Han, X., Deveci, M., & Parmar, M. (2024). A Review of Convolutional Neural Networks in Computer Vision. Artificial Intelligence Review, 57(99), 1–43. https://doi.org/10.1007/s10462-024-10721-6
Zhou, L., Dong, Y., Ma, B., Yin, Z., & Lu, F. (2025). Object Detection in Low-Light Conditions based on DBS-YOLOv8. Cluster Computing, 28(55). https://doi.org/10.1007/s10586-024-04829-1
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Aulya Anggita Putri Selendra, Teguh Arifianto, Fathurrozi Winjaya
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
