Hybrid Approach for Early Road Defect Detection: Integrating Edge Detection with Attention-Enhanced MobileNetV3 for Superior Classification

The early detection of road defects is critical for maintaining infrastructure quality and ensuring public safety. This research presents a hybrid approach that combines edge detection techniques with an enhanced deep learning model for efficient and accurate road defect classification. The process begins with edge detection to highlight structural irregularities, such as cracks and potholes, by emphasizing critical features in road surface images. These pre-processed images are then fed into a classification model based on MobileNetV3, augmented with an attention mechanism to improve feature weighting and model focus on defect-prone regions. The proposed system was evaluated on a Crack500 dataset of road surface images, achieving a classification accuracy of 96.2%. This demonstrates significant improvement compared to baseline models without edge detection or attention enhancements. The edge detection stage efficiently reduces noise, while the attention-augmented MobileNetV3 ensures robust feature discrimination, making the approach suitable for real-time and resource-constrained deployment scenarios. This study highlights the effectiveness of combining classical image processing with advanced neural network techniques. The proposed system has the potential to optimize road maintenance workflows, operational costs, and improve road safety by enabling early and precise defect identification.

@article{Oulahyane2025,
  title     = {Hybrid Approach for Early Road Defect Detection: Integrating Edge Detection with Attention-Enhanced MobileNetV3 for Superior Classification},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {4},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Ayoub Oulahyane and Mohcine Kodad and El Houcine Addou and Sofia Ourarhi and Hajar Chafik},
  doi       = {10.14569/IJACSA.2025.0160484},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160484}
}