Pavement Distress

Pavement distress, encompassing various types of damage like cracks and potholes, is studied to improve road maintenance and safety. Current research heavily utilizes deep learning, employing object detection models (like YOLO and its variants) and semantic segmentation techniques to automatically identify and classify these distresses from images, often captured by UAVs or ground-based cameras. This automated approach offers significant advantages over manual inspection, leading to more efficient resource allocation and improved road infrastructure management, particularly in resource-constrained settings. The development of large, diverse datasets and the exploration of efficient model architectures are key focuses to enhance accuracy and real-time capabilities.