Road Pothole Detection Using Deep Learning

Road surface deterioration, particularly potholes, poses significant hazards to drivers and contributes to vehicle damage and traffic accidents. Traditional detection methods, including manual inspections and cloud-based systems, suffer from high latency, limited scalability, and require extensive labeled datasets. This paper presents a real-time, deep learning-based system for detecting road potholes using edge computing. The proposed method leverages convolutional neural networks (CNNs) to identify potholes from road imagery, while minimizing data transmission delays by processing information locally. To enhance model performance, techniques such as data augmentation and semi-supervised learning are incorporated. Evaluation metrics including precision, recall, and mean Average Precision (mAP) confirm the system's effectiveness across varied environments. This work demonstrates the viability of deploying intelligent, lowlatency pothole detection systems on edge devices, offering a scalable and cost-effective solution for improving road safety and infrastructure maintenance.