Detecting Road Defects with Computer Vision

Road infrastructure is crucial yet often inadequately monitored, leading to deterioration that can go unnoticed until it becomes severe. A solution to this challenge is the use of computer vision for automated road condition assessment, which can replace the current manual inspection process. This approach involves using a camera on an inspection vehicle or drone to capture road imagery, which is then analyzed by a trained detection model to identify and classify defects such as potholes and cracks. The tutorial outlines building a road defect detection system using a custom RF-DETR model deployed through Roboflow Workflow, with two methods for data collection: UAV aerial imagery and dashcam video. The UAV method uses drones to capture high-resolution images, which are processed using the SAHI technique to detect defects, while the dashcam method involves continuous video recording by an inspection vehicle, with video frames analyzed for defects using ByteTrack for tracking. Both methods generate automated, structured inspection reports, providing maintenance teams with detailed, timestamped records to facilitate timely repairs. The system is designed to enhance road maintenance efforts by providing accurate defect counts and enabling trend tracking, although it still requires human oversight for final decision-making.