Road Surface Defects and Utilities Detection using YOLO26 with Explainable AI

1. We conduct a comprehensive comparative evaluation of six YOLO models (YOLOv7, YOLOv8, YOLOv9, YOLOv10, YOLOv11, and YOLO26) for multi-class road surface anomaly detection.

2. We design a multi-class detector that is capable of identifying potholes, drain holes and sewer covers correctly based on the images of the roads.

3. We use explainable AI, grounded on Grad-CAM visualization, to increase model transparency, and to gain insight into spatial attention behavior on model.

4. We analyze the models using conventional object detection metrics and show that YOLO26 has the greatest overall detection performance on the studied dataset.