Road Condition Monitoring

Road condition monitoring aims to automate the assessment of road infrastructure integrity and safety, primarily using image-based analysis. Current research heavily utilizes deep learning models, such as convolutional neural networks (CNNs) including YOLO and Faster R-CNN architectures, to detect and classify road damage (e.g., potholes, cracks) and assess overall road quality. These advancements enable more efficient and cost-effective maintenance strategies, improving road safety and reducing infrastructure costs. The development of large, labeled datasets is crucial for training and validating these models, facilitating progress in this rapidly evolving field.