DEEP LEARNING-BASED AUTOMATED DETECTION OF ROAD SURFACE DAMAGE USING UAV IMAGERY

Abstract

Automated road surface damage detection has become a critical requirement for modern intelligent transportation and maintenance systems, driven by the increasing need for rapid, cost-effective, and accurate assessment of infrastructure conditions. Recent studies demonstrate that combining Unmanned Aerial Vehicles (UAVs) with deep learning provides a powerful, scalable solution for real-time monitoring and high-precision defect identification [1], [2], [3]. UAV imagery offers wide-area coverage, reduced inspection time, and the ability to capture high-resolution road surface data even in complex environments, outperforming traditional manual and vehicle-mounted inspections [4], [5], [6]. Deep learning architectures such as CNNs, YOLO variants, VGG-19, and optimized lightweight networks have shown significant success in detecting cracks, potholes, rutting, and other pavement distress types with remarkable accuracy and robustness [7], [8], [9], [10]. Large-scale datasets like RDD and multisource UAV-based benchmark datasets have further enhanced model generalizability and cross-regional validation [11], [12], [13]. Researchers also highlight the advantages of advanced attention-based models and real-time inference techniques that achieve superior precision while maintaining computational efficiency for drone-based deployment [14], [15], [16]. Recent frameworks integrating hierarchical feature extraction, patch-level inference, and multi-modal imaging (RGB and LiDAR) demonstrate improved detection performance under varying illumination, shadows, and occlusion conditions [17], [18], [19], [20], [21]. Moreover, newly emerging models such as YOLOv8, YOLOv9-based PavD, and improved VGG networks continue to push the boundaries of automated pavement distress detection [22], [23], [24], [25]. This study presents a deep learning-driven, UAV-enabled automated road surface damage detection system designed for accurate, fast, and scalable road condition assessment, ultimately supporting smart city infrastructure and efficient road maintenance planning.