A Review on Deep Learning and IoT-Based Road Surface Damage Detection

Abstract

Road surface deterioration, particularly potholes and cracks, poses serious challenges to transportation safety, vehicle maintenance, and infrastructure management. Traditional road inspection methods rely heavily on manual surveys and sensor-based monitoring, which are often time-consuming, costly, and limited in coverage. With recent advancements in computer vision, deep learning, and Internet of Things (IoT) technologies, automated road damage detection systems have gained significant research attention. This paper presents a comprehensive review of existing techniques for road surface damage detection, focusing on traditional image processing methods, sensor-based approaches, and modern deep learning-based solutions.The review highlights the effectiveness of convolutional neural networks and object detection frameworks such as YOLO in identifying potholes and other road anomalies with high accuracy and real-time performance. Furthermore, the integration of IoT devices, edge computing platforms, and GPS-based geo-tagging systems has enabled scalable and intelligent road monitoring solutions. The paper also discusses the advantages, limitations,
and practical challenges associated with various approaches, including computational complexity, environmental variability,
and deployment constraints. Finally, potential future research directions are outlined, emphasizing the need for lightweight
models, large-scale datasets, and smart transportation integration. This review aims to provide researchers and practitioners
with a consolidated understanding of current advancements and emerging trends in intelligent road surface damage detection
systems.