Pothole Detection with RF-DETR

Maintaining safe transportation infrastructure is a significant challenge for modern cities due to the presence of potholes, which cause vehicle damage and safety hazards. Traditional manual inspection methods are often too slow, making automated systems a necessity. This guide outlines the development of a pothole tracking prototype using the Roboflow platform, leveraging computer vision technology to detect road defects from moving vehicles. By using the RF-DETR architecture, a transformer-based model engineered for low-latency inference, the system can accurately track hazards across video frames. The process involves establishing a development environment, sourcing diverse datasets, precise labeling, and training the model to ensure high detection precision. The workflow integrates various components like Byte Tracker to maintain temporal consistency and visualization blocks to render and display detected potholes in real-time. Additionally, the setup allows for intelligent data analysis, counting potholes, and determining repair urgency. By employing Roboflow Workflows, municipalities can transform raw video feeds into actionable data for infrastructure maintenance, optimizing road repair strategies efficiently.