AUTOMATED ROAD DAMAGE DETECTION USING UAV IMAGES AND DEEP LEARNING TECHNIQUES

Abstract

Road infrastructure plays a vital role in ensuring safe and efficient transportation. Timely detection of road damage such as cracks, potholes, and surface deformations is essential for reducing maintenance costs and preventing accidents. Traditional road inspection methods rely on manual surveys, which are time-consuming, labor-intensive, costly, and prone to human error. This project presents an Automated Road Damage Detection System Using UAV Images and Deep Learning Techniques to improve the efficiency and accuracy of road condition assessment. Unmanned Aerial Vehicles (UAVs), commonly known as drones, are used to capture high-resolution aerial images of road surfaces from different locations. These images are then preprocessed to enhance quality by reducing noise, correcting illumination, and resizing for uniformity. A deep learning-based Convolutional Neural Network (CNN) or advanced object detection models such as YOLO are employed to automatically identify and classify different types of road damages, including longitudinal cracks, transverse cracks, alligator cracks, and potholes.