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 real
time 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