Pothole Detection Using Deep Learning

Abstract

Potholes are a significant road infrastructure problem that poses safety risks to drivers and causes substantial damage to vehicles. Accurate and real time detection of potholes is crucial for timely repairs and road maintenance. This paper presents a novel approach for pothole detection using YOLOv8, an advanced version of the You Only Look Once (YOLO) object detection algorithm. The proposed method leverages the advantages of YOLOv8, which combines state-of-the-art object detection techniques with improved network architecture for enhanced accuracy and efficiency. A comprehensive dataset of road images containing various pothole instances is collected and used to train the YOLOv8 model. Transfer learning techniques are employed to fine-tune the model for pothole detection specifically. To evaluate the performance of the proposed method, extensive experiments are conducted on different road scenarios, including varying lighting conditions, road types, and pothole sizes. The results demonstrate that the YOLOv8-based pothole detection system achieves high accuracy and real-time processing capability, making it suitable for deployment in smart city infrastructure and vehicle navigation systems. The contributions of this work lie in the development of an effective and efficient pothole detection system using YOLOv8, capable of identifying potholes accurately in real-world scenarios. This system can significantly aid road maintenance authorities in identifying potholes promptly, facilitating timely repairs and ultimately leading to safer and smoother road conditions for drivers and pedestrians alike.