Performance Analysis of YOLO26 in Pothole Detection on an Indonesian Road Dataset
DOI:
https://doi.org/10.30871/jaic.v10i4.13585Keywords:
YOLO26l, Object Detection, Deep Learning, Road Damage Detection, Road Damage Indonesia DatasetAbstract
Road damage is one of the infrastructure problems that can compromise safety, comfort, and the smooth flow of traffic. The road inspection process, which is still carried out manually, requires a relatively large amount of time, labor, and cost, making a more efficient method necessary. Advances in computer vision and deep learning technologies enable the automatic detection of road damage through an object detection approach. This study aims to analyze the performance of the YOLO26l model as a baseline model in detecting four categories of road damage such as potholes, alligator cracking, lateral cracking, and longitudinal cracking using the Road Damage Indonesia Dataset. The dataset was divided into 70% training data, 15% validation data, and 15% testing data. The training process was conducted using the pre-trained weights from yolo26l.pt via the Ultralytics framework without any architectural modifications or the application of image enhancement methods. Performance evaluation was conducted using the Precision, Recall, [email protected] ([email protected]), and [email protected]:0.95 ([email protected]:0.95) metrics. The results of the study show that the YOLO26l model achieved a Precision of 0.7028, a Recall of 0.6492, a [email protected] of 0.6890, and a [email protected]:0.95 of 0.3391. Analysis using a confusion matrix, precision–recall curve, and visualization of the detection results showed that the model was able to identify all four categories of road damage well, although there were still some objects that went undetected under poor lighting conditions, due to small object sizes, or complex road surface textures. Based on these results, it can be concluded that YOLO26l performs well as a baseline model for road damage detection on the Indonesian road dataset
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[1] Y. Zhang and L. Cheng, “The role of transport infrastructure in economic growth: Empirical evidence in the UK,” Transp. Policy (Oxf)., vol. 133, pp. 223–233, Mar. 2023, doi: 10.1016/j.tranpol.2023.01.017.
[2] M. A. Rasee et al., “AI-driven Vision-based Pothole Detection for Improved Road Safety,” Pertanika J. Sci. Technol., vol. 33, no. 3, Apr. 2025, doi: 10.47836/pjst.33.3.20.
[3] R. A. Gumelar and A. Susetyaningsih, “Pengaruh Kerusakan Jalan Terhadap Kenyamanan Pengguna Jalan di Jalan Raya,” Jurnal Konstruksi, vol. 21, no. 2, pp. 265–274, Oct. 2023, doi: 10.33364/konstruksi/v.21-2.1416.
[4] X. Yang, J. Zhang, W. Liu, J. Jing, H. Zheng, and W. Xu, “Automation in road distress detection, diagnosis and treatment,” Mar. 01, 2024, KeAi Publishing Communications Ltd. doi: 10.1016/j.jreng.2024.01.005.
[5] R. Fan, S. Guo, L. Wang, and M. J. Bocus, “Computer-Aided Road Inspection: Systems and Algorithms,” Mar. 2022, doi: 10.48550/arXiv.2203.02355.
[6] A. B. Amjoud and M. Amrouch, “Object Detection Using Deep Learning, CNNs and Vision Transformers: A Review,” IEEE Access, vol. 11, pp. 35479–35516, 2023, doi: 10.1109/ACCESS.2023.3266093.
[7] Z. Zou, K. Chen, Z. Shi, Y. Guo, and J. Ye, “Object Detection in 20 Years: A Survey,” Proceedings of the IEEE, vol. 111, no. 3, pp. 257–276, Mar. 2023, doi: 10.1109/JPROC.2023.3238524.
[8] M. L. Ali and Z. Zhang, “The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection,” Dec. 01, 2024, Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/computers13120336.
[9] B. Fan and X. Song, “A Review of Deep Learning Based Object Detection Algorithms,” Journal of Engineering Research and Reports, vol. 26, no. 11, pp. 88–99, Oct. 2024, doi: 10.9734/jerr/2024/v26i111316.
[10] M. N. Andrean et al., “Comparing Haar Cascade and YOLOFACE for Region of Interest Classification in Drowsiness Detection,” JURNAL MEDIA INFORMATIKA BUDIDARMA, vol. 8, no. 1, p. 272, Jan. 2024, doi: 10.30865/mib.v8i1.7167.
[11] H. Kusumah, M. R. Nurholik, C. P. Riani, and I. R. Nur Rahman, “Deep Learning for Pothole Detection on Indonesian Roadways,” Journal Sensi, vol. 9, no. 2, pp. 175–186, Aug. 2023, doi: 10.33050/sensi.v9i2.2911.
[12] L. V. Fortin and O. E. Llantos, “Performance Analysis of YOLO versions for Real-time Pothole Detection,” in Procedia Computer Science, Elsevier B.V., 2025, pp. 77–84. doi: 10.1016/j.procs.2025.03.013.
[13] R. Febriyanti Puspita, M. Naufal, and F. Al Zami, “Improving YOLO Performance with Advanced Data Augmentation for Soccer Object Detection,” 2025. [Online]. Available: http://jurnal.polibatam.ac.id/index.php/JAIC
[14] R. C. Subianto, M. Naufal, and F. Alzami, “Improving YOLO12 Performance Using Efficient Channel Attention For Ship Object Detection,” 2026. [Online]. Available: http://jurnal.polibatam.ac.id/index.php/JAIC
[15] S. S. Park, V. T. Tran, and D. E. Lee, “Application of various yolo models for computer vision-based real-time pothole detection,” Applied Sciences (Switzerland), vol. 11, no. 23, Dec. 2021, doi: 10.3390/app112311229.
[16] G. Jocher, J. Qiu, M. Liu, S. Lyu, F. C. Akyon, and M. E. Kalfaoglu, “Ultralytics YOLO26: Unified Real-Time End-to-End Vision Models,” Jun. 2026, [Online]. Available: http://arxiv.org/abs/2606.03748
[17] R. Padilla, W. L. Passos, T. L. B. Dias, S. L. Netto, and E. A. B. Da Silva, “A comparative analysis of object detection metrics with a companion open-source toolkit,” Electronics (Switzerland), vol. 10, no. 3, pp. 1–28, Feb. 2021, doi: 10.3390/electronics10030279.
[18] T. Diwan, G. Anirudh, and J. V. Tembhurne, “Object detection using YOLO: challenges, architectural successors, datasets and applications,” Multimed. Tools Appl., vol. 82, no. 6, pp. 9243–9275, Mar. 2023, doi: 10.1007/s11042-022-13644-y.
[19] B. M. Hussein and S. M. Shareef, “An Empirical Study on the Correlation between Early Stopping Patience and Epochs in Deep Learning,” ITM Web of Conferences, vol. 64, p. 01003, 2024, doi: 10.1051/itmconf/20246401003.
[20] S. S. A. Zaidi, M. S. Ansari, A. Aslam, N. Kanwal, M. Asghar, and B. Lee, “A Survey of Modern Deep Learning based Object Detection Models,” May 2021, [Online]. Available: http://arxiv.org/abs/2104.11892.
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