Image-Based Road Damage Detection Using Deep Learning Models

Muchamad Sobri Sungkar; Alex Wenda

doi:10.35877/454RI.jinav4895

PDF Get rights and content

JINAV: Journal of Information and Visualization

Vol. 7 No. 2 (2026), Pages 159-171 · 2026-08-31

OriginalPaper | Open access | Published: August 31, 2026

Image-Based Road Damage Detection Using Deep Learning Models

Muchamad Sobri Sungkar, Alex Wenda

JINAV: Journal of Information and Visualization, Vol. 7 No. 2 (2026), pp. 159-171 https://doi.org/10.35877/454RI.jinav4895 Published: 2026-08-31

Get Permissions

Abstract

Road infrastructure deteriorates continuously under traffic load and weather, and undetected damage such as potholes, surface cracks, and uneven asphalt increases accident risk and long-term repair cost. Traditional road surveys rely on manual visual inspection, which is slow, subjective, and difficult to scale across large networks. This study develops an image-based road damage detection system built on deep learning models that automatically locate and classify damage from road surface images. A curated dataset of 160 annotated road images covering four categories—Normal Road, Potholes, Road Cracks, and Severe Damage—was preprocessed through resizing and normalization and expanded with data augmentation including flipping, rotation, brightness shift, and noise injection. Three architectures were trained and compared: a baseline Convolutional Neural Network (CNN), a lightweight MobileNet classifier, and a YOLOv8 object detector. Models were evaluated using accuracy, precision, recall, F1-score, and mean Average Precision (mAP). YOLOv8 achieved the strongest results with 94.60% accuracy, 94.00% F1-score, and 92.80% mAP, outperforming MobileNet and the baseline CNN while still supporting near real-time inference. A browser-based application was implemented to upload road images, preview them, run a detection simulation, and display class labels, confidence scores, and bounding boxes over pothole regions. A moving-vehicle simulation further demonstrates real-time risk reporting, raising the warnings “Risk Detected,” “Vehicle Passing Damaged Road,” and “Maintenance Required” when the vehicle crosses a damaged zone. The contributions are a comparative deep learning study for road damage detection, an interactive detection-and-simulation prototype, and a practical workflow for automated road condition monitoring

Keywords

Road Damage Detection Deep Learning Convolutional Neural Network YOLOv8 Computer Vision

References (27)

Alam, N. A., Ahsan, M., Based, M. A., & Haider, J. (2021). Intelligent System for Vehicles Number Plate Detection and Recognition Using Convolutional Neural Networks. Technologies, 9(1), 1–18. https://doi.org/10.3390/technologies9010009
Alamsyah, D., & Pratama, D. (2019). Deteksi Ujung Jari menggunakan Faster-RCNN dengan Arsitektur Inception v2 pada Citra Derau. JuSiTik?: Jurnal Sistem Dan Teknologi Informasi Komunikasi, 2(1), 1. https://doi.org/10.32524/jusitik.v2i1.435
Almasri, I., Manasreh, D., & Nazzal, M. D. (2025). AI Meets ADAS: Intelligent Pothole Detection for Safer AV Navigation. Vehicles, 7(4), 109. https://doi.org/10.3390/vehicles7040109
Altini, N., De Giosa, G., Fragasso, N., Coscia, C., Sibilano, E., Prencipe, B., Hussain, S. M., Brunetti, A., Buongiorno, D., Guerriero, A., Tatò, I. S., Brunetti, G., Triggiani, V., & Bevilacqua, V. (2021). Segmentation and Identification of Vertebrae in CT Scans Using CNN, k-Means Clustering and k-NN. Informatics, 8(2), 40. https://doi.org/10.3390/informatics8020040
Arief, L., Tantowi, A. Z., Novani, N. P., & Sundara, T. A. (2020). Implementation of YOLO and smoke sensor for automating public service announcement of cigarette’s hazard in public facilities. 2020 International Conference on Information Technology Systems and Innovation, ICITSI 2020 - Proceedings, 101–107. https://doi.org/10.1109/ICITSI50517.2020.9264972
Atika, L., Nurmaini, S., Partan, R. U., & Sukandi, E. (2022). Image Segmentation for Mitral Regurgitation with Convolutional Neural Network Based on UNet, Resnet, Vnet, FractalNet and SegNet: A Preliminary Study. In Big Data and Cognitive Computing (Vol. 6, Number 4). https://doi.org/10.3390/bdcc6040141
Balderas, L., Lastra, M., & Benítez, J. M. (2024). Optimizing Convolutional Neural Network Architectures. Mathematics, 12(19), 1–25. https://doi.org/10.3390/math12193032
Bandaru, S. B., Deivarajan, N., & Gatram, R. M. B. (2022). An Optimized Deep Learning Techniques for Analysing Mammograms. International Journal of Engineering Trends and Technology, 70(7), 388–398. https://doi.org/10.14445/22315381/IJETT-V70I7P240
Bhatt, A. K., Raj, H., Sharma, V. B., Biswas, S., Singh, A., Silori, R., & Pandey, M. (2025). Advancements in pothole detection techniques: a comprehensive review and comparative analysis. Discover Artificial Intelligence, 5(1), 255. https://doi.org/10.1007/s44163-025-00297-7
Dewa, A. L. (2023). Empirical Analysis of Street Safety: Driver Behavior and Traffic Characteristics on Traffic Accidents in Semarang. Research Horizon, 3(5), 499–508.
Dhonde, S., Mirani, J., Patwardhan, S., & Bhurchandi, K. M. (2022). Over-Speed and License Plate Detection of Vehicles. Proceedings of PCEMS 2022 - 1st International Conference on the Paradigm Shifts in Communication, Embedded Systems, Machine Learning and Signal Processing, 113–118. https://doi.org/10.1109/PCEMS55161.2022.9808085
Gao, J., French, A., Pound, M., He, Y., Pridmore, T., & Pieters, J. (2020). Deep convolutional neural networks for image-based Convolvulus sepium detection in sugar beet fields. Plant Methods, 16. https://doi.org/10.1186/s13007-020-00570-z
Gengeç, N., Eker, O., Çevi?Kalp, H., Yazici, A., & Yavuz, H. S. (2021). Visual object detection for autonomous transport vehicles in smart factories. Turkish Journal of Electrical Engineering and Computer Sciences, 29(4), 2101–2115. https://doi.org/10.3906/ELK-2008-62
Gunawan, G., Nuriyanto, H., Sriadhi, S., Fauzi, A., Usman, A., Fadlina, F., Dafitri, H., Simarmata, J., Utama Siahaan, A. P., & Rahim, R. (2018). Mobile Application Detection of Road Damage using Canny Algorithm. Journal of Physics: Conference Series, 1019(1), 012035. https://doi.org/10.1088/1742-6596/1019/1/012035
Hong, J., Tamakloe, R., & Park, D. (2020). Discovering Insightful Rules among Truck Crash Characteristics using Apriori Algorithm. Journal of Advanced Transportation, 2020. https://doi.org/10.1155/2020/4323816
Jeon, Y.-D., Kang, M.-J., Kuh, S.-U., Cha, H.-Y., Kim, M.-S., You, J.-Y., Kim, H.-J., Shin, S.-H., Chung, Y.-G., & Yoon, D.-K. (2024). Deep Learning Model Based on You Only Look Once Algorithm for Detection and Visualization of Fracture Areas in Three-Dimensional Skeletal Images. Diagnostics, 14(1). https://doi.org/10.3390/diagnostics14010011
Kaushik, V., & Kalyan, B. S. (2022). Pothole Detection System: A Review of Different Methods Used for Detection. 2022 Second International Conference on Computer Science, Engineering and Applications (ICCSEA), 1–4. https://doi.org/10.1109/ICCSEA54677.2022.9936360
Kavya, T. S., Jang, Y. M., Peng, T., & Cho, S. B. (2020). Vehicle detection and tracking from a video captured by moving host. Indian Journal of Computer Science and Engineering, 11(3), 226–235. https://doi.org/10.21817/indjcse/2020/v11i3/201103187
Li, L., Martin, T., & Xu, X. (2020). A novel vision-based real-time method for evaluating postural risk factors associ- ated with musculoskeletal disorders. Applied Ergonomics, 87(September), 103138. https://doi.org/10.1016/j.apergo.2020.103138
Lubna, Mufti, N., & Shah, S. A. A. (2021). Automatic Number Plate Recognition:A Detailed Survey of Relevant Algorithms. Sensors, 21(9), 3028. https://doi.org/10.3390/s21093028
Mirwansyah, D., & Arief Wibowo. (2022). Fruit Image Classification Using Deep Learning Algorithm: Systematic Literature Review (Slr). Multica Science and Technology (Mst) Journal, 2(2), 120–123. https://doi.org/10.47002/mst.v2i2.356
Patil, S. S., Patil, S. H., Pawar, A. M., Bewoor, M. S., Kadam, A. K., Patkar, U. C., Wadare, K., & Sharma, S. (2023). Vehicle Number Plate Detection using YoloV8 and EasyOCR. 2023 14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023, 1–4. https://doi.org/10.1109/ICCCNT56998.2023.10307420
Prahara, A., Pranolo, A., & Dre?ewski, R. (2015). GPU Accelerated Number Plate Localization in Crowded Situation. International Journal of Advances in Intelligent Informatics, 1(3), 150–157. https://doi.org/10.26555/ijain.v1i3.46
Ruseruka, C., Mwakalonge, J., Comert, G., Siuhi, S., Ngeni, F., & Anderson, Q. (2024). Augmenting roadway safety with machine learning and deep learning: Pothole detection and dimension estimation using in-vehicle technologies. Machine Learning with Applications, 16, 100547. https://doi.org/10.1016/j.mlwa.2024.100547
Shovo, S. U. A., Abir, M. G. R., Kabir, M. M., & Mridha, M. F. (2024). Advancing low-light object detection with you only look once models: An empirical study and performance evaluation. Cognitive Computation and Systems, 6(4), 119–134. https://doi.org/https://doi.org/10.1049/ccs2.12114
Sun, X., Wu, P., & Hoi, S. C. H. (2018). Face detection using deep learning: An improved faster RCNN approach. Neurocomputing, 299, 42–50. https://doi.org/10.1016/J.NEUCOM.2018.03.030
Yalçin, N., & Alisawi, M. (2024). Introducing a novel dataset for facial emotion recognition and demonstrating significant enhancements in deep learning performance through pre-processing techniques. Heliyon, 10(20), 1–22. https://doi.org/10.1016/j.heliyon.2024.e38913

How to Cite

Muchamad Sobri Sungkar, & Alex Wenda. (2026). Image-Based Road Damage Detection Using Deep Learning Models. JINAV: Journal of Information and Visualization, 7(2), 159–171. https://doi.org/10.35877/454RI.jinav4895