Pavement Image

Pavement image analysis uses computer vision techniques to automatically assess pavement conditions, primarily focusing on detecting and classifying distresses like potholes and cracks, and estimating overall pavement condition indices (PCI). Research heavily employs convolutional neural networks (CNNs), such as ResNet and MobileNet, often incorporating transfer learning and multi-task learning frameworks to improve efficiency and accuracy in tasks ranging from simple binary classification to complex segmentation and PCI prediction. These advancements offer significant potential for automating road inspections, improving infrastructure maintenance planning, and enhancing road safety by enabling timely identification of hazardous conditions.