Automated Pothole Detection: A Survey on Vision-Based and Wavelet-Enhanced Deep Learning Approaches

Automated pothole detection is crucial for road safety and infrastructure management. This survey reviewed sensor-based, traditional vision-based, and deep learning methods, highlighting the limitations of spatial-domain features and the benefits of multi-resolution analysis using wavelet transforms. The integration of YOLOv10 with LWT demonstrates significant improve-ments in detecting small and distant potholes under diverse environmental conditions. Future research should emphasize multi-modal data fusion, real-time edge deployment, and severity assessment to enable fully autonomous and robust pothole monitoring systems.  

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