Current - Issue

1. 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, vol. 11, pp. 846-862, 2024.
2. D. Arya et al., "RDD2022: A multi-national image dataset for automatic Road Damage Detection," arXiv:2209.08538, 2022.
3. RDD2022, "The multi-national Road Damage Dataset released through CRDDC'2022," Figshare, 2022.
4. "Real-time pothole detection using YOLO models: An efficient and cost-effective solution for infrastructure monitoring," International
Journal of Transportation Science and Technology, 2026.
5. "Robust multi-weather pothole detection: An enhanced YOLOv9 trained on the MWPD dataset," Results in Engineering, 2026.
6. P. A. Chitale, K. Y. Kekre, H. R. Shenai, R. Karani, and J. P. Gala, "Pothole Detection and Dimension Estimation System using DeepLearning (YOLO) and Image Processing," 2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ),
2020, pp. 1-6, doi: 10.1109/IVCNZ51579.2020.9290547.
7. M. Ren, X. Zhang, X. Chen, B. Zhou, and Z. Feng, "YOLOv5s-M: A deep learning network model for road pavement damage detection
from urban street-view imagery," International Journal of Applied Earth Observation and Geoinformation, vol. 120, 2023, Art. no. 103335,
doi: 10.1016/j.jag.2023.103335.
8. J. Zeng and H. Zhong, "YOLOv8-PD: an improved road damage detection algorithm based on YOLOv8n model," Scientific Reports, vol.
14, Art. no. 12052, 2024.
9. "Road damage detection and classification using deep neural networks," Discover Applied Sciences, 2024.
10. "Computer Vision Based Pothole Detection under Challenging Conditions," Sensors, vol. 22, no. 22, Art. no. 8878, 2022, doi:
10.3390/s22228878.
11. "Augmenting roadway safety with machine learning and deep learning: Pothole detection and dimension estimation using in-vehicle
technologies," 2024.
12. "Road damage detection based on improved YOLO algorithm," Scientific Reports, 2025.
13. V. Pham, L. D. T. N. Dong, and D.-L. Bui, "Optimizing YOLO Architectures for Optimal Road Damage Detection and Classification: A
Comparative Study from YOLOv7 to YOLOv10," arXiv: 2410.08409, 2024.
14. H. Chen, Z. Tu, Y. Zhao, and J. Yet, "A high-resolution perspective-view road image dataset for pothole detection," Scientific Data, vol. 13,
Art. No. 961, 2026.
15. M. Yurdakul and S. Tasdemir, "An Enhanced YOLOv8 Model for Real-Time and Accurate Pothole Detection and Measurement," arXiv:
2505.04207, 2025.
16. "Advancements in real-time road damage detection: a comprehensive survey of methodologies and datasets," Journal of Real-Time Image
Processing, 2025.
17. "Road Damage Detection Based on Improved YOLO Algorithm," Scientific Reports, 2025.
18. "Performance Analysis of YOLO26 in Pothole Detection on an Indonesian Road Dataset," Journal of Applied Informatics and Computing,
2026.
19. H. Yang, Y. Song, Y. Liang, E. Tang, and D. Cao, "SDC-YOLOv8: An Improved Algorithm for Road Defect Detection through Attention-
Enhanced Feature Learning and Adaptive Feature Reconstruction," Sensors, vol. 26, no. 2, Art. no. 609, 2026, doi: 10.3390/s26020609.
20. X. Wang, H. Gao, Z. Jia, and Z. Li, "BL-YOLOv8: An Improved Road Defect Detection Model Based on YOLOv8," Sensors, vol. 23, no.
20, Art. no. 8361, 2023, doi: 10.3390/s23208361.