Automated Road Damage Detection Using UAV Images and Deep Learning Techniques

Abstract

Road infrastructure plays a crucial role in economic development, yet maintaining it is challenging due to frequent damage caused by environmental factors and heavy traffic. Traditional road inspection methods are time-consuming, labour-intensive, and prone to human error. To address these limitations, this study presents an automated road-damage-detection system using Unmanned Aerial Vehicle (UAV) images and deep-learning techniques. High-resolution UAV images are collected and processed to detect various types of road damage such as cracks, potholes, and surface deformations, and a Convolutional Neural Network (CNN)-based model is trained to classify and segment road defects. Transfer learning and data-augmentation techniques are applied to enhance model performance on diverse road conditions, and complementary detectors based on YOLO and Faster R-CNN are considered for realtime damage localisation. The proposed system enables cost-effective and scalable road monitoring, facilitating timely maintenance and reducing repair costs. The prototype is implemented in Python using TensorFlow, OpenCV, NumPy, and pandas, processes UAV imagery, and produces damage classifications that can support proactive maintenance decisions. Experimental observations demonstrate the effectiveness of the approach, with the system reporting superior accuracy compared with traditional manual inspection. This research contributes to smart transportation infrastructure by providing an efficient and automated solution for road-damage assessment, ultimately improving road safety and longevity.