Deep Learning Based- Pothole Detection

Abstract

Pothole detection is an essential task in road maintenance and transportation safety, as damaged roads contribute significantly to vehicle damage, accidents, and economic loss. Traditional pothole detection methods, such as manual inspections and sensor-based approaches, suffer from inefficiency, inaccuracy, and high operational costs. With the rise of deep learning and computer vision techniques, automated pothole detection has become a viable solution. This survey provides a comprehensive review of pothole detection systems, focusing on deep learning- based methods, particularly YOLO (You Only Look Once) variants. Multiple versions, including YOLOv3, YOLOv4, YOLOv5, YOLOv7, and YOLOv8, are analyzed for their detection accuracy, computational efficiency, and real-time applicability. Furthermore, this paper compares various studies that have employed deep learning models for pothole detection, highlighting the advantages and challenges of each approach. The paper also discusses GPS- based reporting systems, integration with real-world road maintenance infrastructure, and potential improvements for future research.