Pothole Detection

Pothole detection research focuses on automatically identifying potholes in roads using various imaging techniques, primarily to improve road safety and maintenance efficiency. Current approaches leverage deep learning models, including convolutional neural networks (CNNs) like YOLOv7 and specialized architectures for semantic segmentation and instance segmentation, often incorporating techniques like super-resolution and multi-scale feature fusion to enhance accuracy. These methods utilize both 2D images and 3D point cloud data from sources such as cameras and LiDAR, with a growing emphasis on developing robust and efficient algorithms for real-world deployment and open-source benchmark datasets to facilitate comparative analysis and progress.