A Real-Time Road Damage Monitoring System using YOLOv11 on an Edge Computer
DOI:
https://doi.org/10.21831/jraee.v4i1.2190Keywords:
YOLOv11, GPS, ONNX, Computer Vision, Edge ComputingAbstract
This study developed a real-time road damage detection system on an edge device to address the inefficiency and subjectivity of manual monitoring. Following the Engineering Design Process (EDP), the system uses a YOLOv11 model—trained in PyTorch and converted to ONNX for optimization—to classify four types of road damage on a Raspberry Pi 5, integrated with a GPS module for automatic coordinate recording. Field evaluation showed that after optimization, the system achieved an average processing speed of 3.66 FPS with an inference latency of 277.69 ms, while maintaining efficient resource usage (45.06% RAM, 65.05% CPU load) and an mAP@50 of 0.554. These results demonstrate the feasibility of an autonomous, accurate, real-time road damage detection system on a low-cost edge computing platform, producing geospatial data ready to support operational decision-making in road maintenance.
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References
[1] D. J. B. M. Kementerian Pekerjaan Umum, “Pedoman Sistem Pemeliharaan Jalan Kota (City Road Management System),” Feb. 2025. [Online]. Available: https://binamarga.pu.go.id/index.php/nspk/detail/02pbm2025-pedoman-sistem-pemeliharaan-jalan-kota-city-road-management-system
[2] C.-C. Hsieh, H.-W. Jia, W.-H. Huang, and M.-H. Hsih, “Deep learning-based road pavement inspection by integrating visual information and IMU,” Information, vol. 15, no. 4, p. 239, 2024, doi: 10.3390/info15040239.
[3] K. Mutijarsa, F. A. Alunjati, F. Hidayat, R. Kurnia, and Z. Arsyad, “Smart road damage survey system using machine vision and IoT technology,” 2023 3rd International Conference on Intelligent Cybernetics Technology & Applications (ICICyTA), pp. 391–396, 2023, doi: 10.1109/icicyta60173.2023.10428992.
[4] A. J. Sidqi, F. A. Alunjati, U. Elviani, and F. Hidayat, “Development of longitudinal and transversal crack detection feature in automated road pavement condition survey system using YOLO algorithm,” 2024 International Conference on ICT for Smart Society (ICISS), pp. 1–5, 2024, doi: 10.1109/iciss62896.2024.10751400.
[5] C. Wu, M. Ye, J. Zhang, and Y. Ma, “YOLO-LWNet: A lightweight road damage object detection network for mobile terminal devices,” Sensors, vol. 23, no. 6, p. 3268, 2023, doi: 10.3390/s23063268.
[6] Y. Zanevych, V. Yovbak, O. Basystiuk, N. Shakhovska, S. Fedushko, and S. Argyroudis, “Evaluation of pothole detection performance using deep learning models under low-light conditions,” Sustainability, vol. 16, no. 24, p. 10964, 2024, doi: 10.3390/su162410964.
[7] R. Sapkota, M. Flores-Calero, R. Qureshi, C. Badgujar, U. Nepal, A. Poulose, P. Zeno, U. B. P. Vaddevolu, S. Khan, M. Shoman, H. Yan, and M. Karkee, “YOLO Advances to Its Genesis: A Decadal and Comprehensive Review of the You Only Look Once (YOLO) Series,” Artificial Intelligence Review, vol. 58, Art. no. 274, 2025, doi: 10.1007/s10462-025-11253-3.
[8] S. Jana, A. I. Middya, and S. Roy, “Mobile Crowd Sensing for Road Anomaly Detection Using Deep Learning,” Spatial Information Research, vol. 33, Art. no. 51, 2025, doi: 10.1007/s41324-025-00651-y.
[9] D. Arya, H. Maeda, S. K. Ghosh, D. Toshniwal, and Y. Sekimoto, “RDD2022: A Multi-National Image Dataset for Automatic Road Damage Detection,” Geoscience Data Journal, 2024, doi: 10.1002/gdj3.260.
[10] D. Minott, S. Siddiqui, and R. J. Haddad, “Benchmarking Edge AI Platforms: Performance Analysis of NVIDIA Jetson and Raspberry Pi 5 with Coral TPU,” in 2025 IEEE SoutheastCon, 2025, pp. 1384–1389, doi: 10.1109/SoutheastCon56624.2025.10971592.
