logo

Literature Review

Vol. 28 No. 47 (2026): Journal 47, January - December 2026, Continous publication - Open issue

Automated pavement distress detection for network-level management

DOI:
https://doi.org/10.15517/es79s423
Submitted
August 22, 2026
Published
2026-11-09

Abstract

Automated pavement distress detection has evolved from threshold- and texture-based image processing to deep networks for classification, object detection, and semantic segmentation. Nevertheless, high performance on closed datasets does not ensure that model outputs can support network-level maintenance decisions. This systematic review examined evidence from vehicle-mounted cameras, smartphones, three-dimensional sensors, and unmanned aerial vehicles, with emphasis on geographic generalization, severity quantification, and integration with pavement management systems. Specialized publisher databases and indexes were consulted for 2011-2026, and 40 primary studies were selected through explicit eligibility, extraction, and quality criteria. Object detectors were found suitable for rapid inventories of cracks, potholes, patches, and related defects, whereas segmentation and three-dimensional data were more appropriate for estimating extent, width, area, depth, or volume. Smartphones provide the strongest cost-to-coverage ratio for frequent screening; unmanned aerial vehicles add value for targeted surveys, inaccessible locations, and post-event inspection; and three-dimensional systems remain necessary when geometry drives the maintenance decision. Four persistent limitations were identified: domain shift across countries and seasons, incompatible distress taxonomies, limited external validation, and weak traceability from predictions to condition indices and maintenance treatments. A tiered architecture is proposed that combines mobile screening, selective reinspection, human audit, and georeferenced updating of the management system. The operational objective should not be the maximization of a laboratory metric, but the production of repeatable, calibrated, and economically useful observations per kilometer of road network.

References

  1. Amhaz, R., Chambon, S., Idier, J., & Baltazart, V. (2016). Automatic crack detection on two-dimensional pavement images: An algorithm based on minimal path selection. IEEE Transactions on Intelligent Transportation Systems, 17(10), 2718-2729.https://doi.org/10.1109/TITS.2015.2477675
  2. Arya, D., Maeda, H., Ghosh, S. K., Toshniwal, D., & Sekimoto, Y. (2021b). RDD2020: An annotated image dataset for automatic road damage detection using deep learning. Data in Brief, 36, 107133. https://doi.org/10.1016/j.dib.2021.107133
  3. Arya, D., Maeda, H., Ghosh, S. K., Toshniwal, D., & Sekimoto, Y. (2024). RDD2022: A multi-national image dataset for automatic road damage detection. Geoscience Data Journal, 11, 846-862. https://doi.org/10.1002/gdj3.260
  4. Arya, D., Maeda, H., Ghosh, S. K., Toshniwal, D., Mraz, A., Kashiyama, T., & Sekimoto, Y. (2021a). Transfer learning-based road damage detection for multiple countries. Automation in Construction, 132, 103935. https://doi.org/10.1016/j.autcon.2021.103935
  5. ASTM International. (2024). ASTM D6433-24: Standard practice for roads and parking lots pavement condition index surveys. West Conshohocken, PA: ASTM International. https://doi.org/10.1520/D6433-24
  6. Chambon, S., & Moliard, J.-M. (2011). Automatic road pavement assessment with image processing: Review and comparison. International Journal of Geophysics, 2011, 989354. https://doi.org/10.1155/2011/989354
  7. Chen, X., Liu, C., Chen, L., Zhu, X., Zhang, Y., & Wang, C. (2024). A pavement crack detection and evaluation framework for a UAV inspection system based on deep learning. Applied Sciences, 14(3), 1157. https://doi.org/10.3390/app14031157
  8. Cheng, H., Zhang, A., Wang, D., Shang, J., He, A., Peng, Y., ... Zhan, Y. (2026). JCNet-based multidistress and surface feature detection in pavement images at pixel level. Journal of Computing in Civil Engineering, 40(3). https://doi.org/10.1061/JCCEE5.CPENG-7183
  9. Coenen, T. B. J., & Golroo, A. (2017). A review on automated pavement distress detection methods. Cogent Engineering, 4(1), 1374822. https://doi.org/10.1080/23311916.2017.1374822
  10. Dai, Y., Zhang, T., Zhou, W., Kuang, K., Long, K., Lu, X., & Wang, S. (2025). A semantic-enhanced transformer with adaptive fusion for road damage detection. Computer-Aided Civil and Infrastructure Engineering, 40(31), 6391-6418. https://doi.org/10.1111/mice.70154
  11. Diao, Z., Huang, X., Liu, H., & Liu, Z. (2023). LE-YOLOv5: A lightweight and efficient road damage detection algorithm based on improved YOLOv5. International Journal of Intelligent Systems, 2023, 8879622. https://doi.org/10.1155/2023/8879622
  12. Fei, Y., Wang, K. C. P., Zhang, A., Chen, C., Li, J. Q., Liu, Y., ... Li, B. (2020). Pixel-level cracking detection on 3D asphalt pavement images through deep-learning-based CrackNet-V. IEEE Transactions on Intelligent Transportation Systems, 21(1), 273-284. https://doi.org/10.1109/TITS.2019.2891167
  13. Gan, G., Xu, X., Ding, Y., Zhang, A., Dong, Z., Zhang, H., ... Sun, H. (2025). Pixel-level detection of cracks based on loop semantic diffusion integration. Journal of Computing in Civil Engineering, 39(4). https://doi.org/10.1061/JCCEE5.CPENG-6147
  14. Ghosh, R., & Smadi, O. (2021). Automated detection and classification of pavement distresses using 3D pavement surface images and deep learning. Transportation Research Record, 2675(9), 1359-1374. https://doi.org/10.1177/03611981211007481
  15. Gopalakrishnan, K. (2018). Deep learning in data-driven pavement image analysis and automated distress detection: A review. Data, 3(3), 28. https://doi.org/10.3390/data3030028
  16. Gopalakrishnan, K., Khaitan, S. K., Choudhary, A., & Agrawal, A. (2017). Deep convolutional neural networks with transfer learning for computer vision-based data-driven pavement distress detection. Construction and Building Materials, 157, 322-330. https://doi.org/10.1016/j.conbuildmat.2017.09.110
  17. Hadjidemetriou, G. M., Vela, P. A., & Christodoulou, S. E. (2018). Automated pavement patch detection and quantification using support vector machines. Journal of Computing in Civil Engineering, 32(1). https://doi.org/10.1061/(ASCE)CP.1943-5487.0000724
  18. He, J., Gong, L., Xu, C., Wang, P., Zhang, Y., Zheng, O., ... Sun, Y. (2025). HighRPD: A high-altitude drone dataset of road pavement distress. Data in Brief, 59, 111377. https://doi.org/10.1016/j.dib.2025.111377
  19. He, X., Tang, Z., Deng, Y., Zhou, G., Wang, Y., & Li, L. (2023). UAV-based road crack object-detection algorithm. Automation in Construction, 154, 105014. https://doi.org/10.1016/j.autcon.2023.105014
  20. Huidrom, L., Das, L. K., & Sud, S. K. (2013). Method for automated assessment of potholes, cracks and patches from road surface video clips. Procedia - Social and Behavioral Sciences, 104, 312-321. https://doi.org/10.1016/j.sbspro.2013.11.124
  21. Jahanshahi, M. R., Jazizadeh, F., Masri, S. F., & Becerik-Gerber, B. (2013). Unsupervised approach for autonomous pavement-defect detection and quantification using an inexpensive depth sensor. Journal of Computing in Civil Engineering, 27(6), 743-754. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000245
  22. Koch, C., & Brilakis, I. (2011). Pothole detection in asphalt pavement images. Advanced Engineering Informatics, 25(3), 507-515. https://doi.org/10.1016/j.aei.2011.01.002
  23. Koch, C., Georgieva, K., Kasireddy, V., Akinci, B., & Fieguth, P. (2015). A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure. Advanced Engineering Informatics, 29(2), 196-210. https://doi.org/10.1016/j.aei.2015.01.008
  24. Koch, C., Jog, G. M., & Brilakis, I. (2013). Automated pothole distress assessment using asphalt pavement video data. Journal of Computing in Civil Engineering, 27(4), 370-378. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000232
  25. Li, Y., Ma, J., Zhao, Z., & Shi, G. (2022). A novel approach for UAV image crack detection. Sensors, 22(9), 3305. https://doi.org/10.3390/s22093305
  26. Maeda, H., Kashiyama, T., Sekimoto, Y., Seto, T., & Omata, H. (2021). Generative adversarial network for road damage detection. Computer-Aided Civil and Infrastructure Engineering, 36(1), 47-60. https://doi.org/10.1111/mice.12561
  27. Maeda, H., Sekimoto, Y., Seto, T., Kashiyama, T., & Omata, H. (2018). Road damage detection and classification using deep neural networks with smartphone images. Computer-Aided Civil and Infrastructure Engineering, 33(12), 1127-1141. https://doi.org/10.1111/mice.12387
  28. Mei, Q., & Gül, M. (2020). A cost effective solution for pavement crack inspection using cameras and deep neural networks. Construction and Building Materials, 256, 119397. https://doi.org/10.1016/j.conbuildmat.2020.119397
  29. Methley, A. M., Campbell, S., Chew-Graham, C., McNally, R., & Cheraghi-Sohi, S. (2014). PICO, PICOS and SPIDER: A comparison study of specificity and sensitivity in three search tools for qualitative systematic reviews. BMC Health Services Research, 14, 579. https://doi.org/10.1186/s12913-014-0579-0
  30. Miller, J. S., & Bellinger, W. Y. (2014). Distress identification manual for the Long-Term Pavement Performance Program (5th rev., FHWA-HRT-13-092). McLean, VA: Federal Highway Administration.
  31. Moon, B., & Lee, H. (2024). Development of deep learning pavement extraction and crack segmentation algorithms to analyze UAV images of roadways. Journal of Infrastructure Systems, 30(3). https://doi.org/10.1061/JITSE4.ISENG-2351
  32. Oliveira, H., & Correia, P. L. (2013). Automatic road crack detection and characterization. IEEE Transactions on Intelligent Transportation Systems, 14(1), 155-168. https://doi.org/10.1109/TITS.2012.2208630
  33. Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., ... Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
  34. Radopoulou, S. C., & Brilakis, I. (2017). Automated detection of multiple pavement defects. Journal of Computing in Civil Engineering, 31(2), 04016057. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000623
  35. Ragnoli, A., De Blasiis, M. R., & Di Benedetto, A. (2018). Pavement distress detection methods: A review. Infrastructures, 3(4), 58. https://doi.org/10.3390/infrastructures3040058
  36. Ryu, S.-K., Kim, T., & Kim, Y.-R. (2015). Image-based pothole detection system for ITS service and road management system. Mathematical Problems in Engineering, 2015, 968361. https://doi.org/10.1155/2015/968361
  37. Safaei, N., Smadi, O., Safaei, B., & Masoud, A. (2021). Efficient road crack detection based on an adaptive pixel-level segmentation algorithm. Transportation Research Record, 2675(9), 370-381. https://doi.org/10.1177/03611981211002203
  38. Saha, P. K., Arya, D., & Sekimoto, Y. (2024). Federated learning-based global road damage detection. Computer-Aided Civil and Infrastructure Engineering, 39(14), 2223-2238. https://doi.org/10.1111/mice.13186
  39. Samadzadegan, F., Dadrass Javan, F., Ashtari Mahini, F., Gholamshahi, M., & Nex, F. (2024). Automatic road pavement distress recognition using deep learning networks from unmanned aerial imagery. Drones, 8(6), 244. https://doi.org/10.3390/drones8060244
  40. Sami, N., Sakib, S., Deb, K., & Sarker, I. H. (2023). An improved YOLOv5-based approach for real-time road pavement distress detection. Algorithms, 16(9), 452. https://doi.org/10.3390/a16090452
  41. Shi, Y., Cui, L., Qi, Z., Meng, F., & Chen, Z. (2016). Automatic road crack detection using random structured forests. IEEE Transactions on Intelligent Transportation Systems, 17(12), 3434-3445. https://doi.org/10.1109/TITS.2016.2552248
  42. Sirhan, M., Bekhor, S., & Sidess, A. (2024). Multilabel CNN model for asphalt distress classification. Journal of Computing in Civil Engineering, 38(1). https://doi.org/10.1061/JCCEE5.CPENG-5500
  43. Song, W., Jia, G., Zhu, H., Jia, D., & Gao, L. (2020). Automated pavement crack damage detection using deep multiscale convolutional features. Journal of Advanced Transportation, 2020, 6412562. https://doi.org/10.1155/2020/6412562
  44. Tawfik, G. M., Dila, K. A. S., Mohamed, M. Y. F., Tam, D. N. H., Kien, N. D., Ahmed, A. M., & Huy, N. T. (2019). A step by step guide for conducting a systematic review and meta-analysis with simulation data. Tropical Medicine and Health, 47, 46. https://doi.org/10.1186/s41182-019-0165-6
  45. Wang, K. C. P. (2000). Designs and implementations of automated systems for pavement surface distress survey. Journal of Infrastructure Systems, 6(1), 24-32. https://doi.org/10.1061/(ASCE)1076-0342(2000)6:1(24)
  46. Wang, W., Xu, X., Peng, J., Hu, W., & Wu, D. (2023). Fine-grained detection of pavement distress based on integrated data using digital twin. Applied Sciences, 13(7), 4549. https://doi.org/10.3390/app13074549
  47. Yan, H., & Zhang, J. (2023). UAV-PDD2023: A benchmark dataset for pavement distress detection based on UAV images. Data in Brief, 51, 109692. https://doi.org/10.1016/j.dib.2023.109692
  48. Zakeri, H., Nejad, F. M., & Fahimifar, A. (2017). Image based techniques for crack detection, classification and quantification in asphalt pavement: A review. Archives of Computational Methods in Engineering, 24(4), 935-977. https://doi.org/10.1007/s11831-016-9194-z
  49. Zhang, A., Wang, K. C. P., Fei, Y., Liu, Y., Chen, C., Yang, G., ... Qiu, S. (2019). Automated pixel-level pavement crack detection on 3D asphalt surfaces with a recurrent neural network. Computer-Aided Civil and Infrastructure Engineering, 34(3), 213-229. https://doi.org/10.1111/mice.12409
  50. Zhang, A., Wang, K. C. P., Fei, Y., Liu, Y., Tao, S., Chen, C., ... Li, J. Q. (2018). Deep-learning-based fully automated pavement crack detection on 3D asphalt surfaces with an improved CrackNet. Journal of Computing in Civil Engineering, 32(5), 04018041. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000775
  51. Zhang, A., Wang, K. C. P., Li, B., Yang, E., Dai, X., Peng, Y., ... Chen, C. (2017). Automated pixel-level pavement crack detection on 3D asphalt surfaces using a deep-learning network. Computer-Aided Civil and Infrastructure Engineering, 32(10), 805-819. https://doi.org/10.1111/mice.12297
  52. Zou, Q., Zhang, Z., Li, Q., Qi, X., Wang, Q., & Wang, S. (2019). DeepCrack: Learning hierarchical convolutional features for crack detection. IEEE Transactions on Image Processing, 28(3), 1498-1512. https://doi.org/10.1109/TIP.2018.2878966

Downloads

Download data is not yet available.