Deep Learning-Based Road Damage Detection Using Improved YOLOv8: Model Performance and Implementation

Road infrastructure monitoring plays a crucial role in ensuring safety and economic efficiency; however, conventional manual inspection methods are expensive and resource-intensive. This study presents the development and evaluation of a real-time road damage detection system using the improved YOLOv8 architecture. Building upon the strengths of previous YOLO models, YOLOv8 offers enhanced accuracy and inference speed, making it highly suitable for mobile deployment. The model was trained to identify six common road damage types in Indonesia: potholes, alligator cracks, transverse cracks, longitudinal cracks, edge cracks, and road joints. Utilizing a hybrid dataset of 2,946 images, combining locally collected data and the RDD2020 dataset, the system incorporates mosaic augmentation and optimized preprocessing to improve generalization. The optimized YOLOv8 model achieved a mean Average Precision (mAP@50) of 96.3%, an F1-score of 91%, and an overall accuracy of 91%, demonstrating superior detection and classification performance. The system was deployed as a user-friendly smartphone application, enabling automated, geo-tagged road condition surveys and offering road authorities a scalable, efficient, and practical tool for infrastructure monitoring.

@article{Rahman2026,
  title     = {Deep Learning-Based Road Damage Detection Using Improved YOLOv8: Model Performance and Implementation},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {5},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Aulia Rahman and Hwa Jen Yap and Rusdha Muharar},
  doi       = {10.14569/IJACSA.2026.0170585},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170585}
}