Pothole Detection using YOLO Deep Learning Model

Abstract

Pothole detection using deep learning, particularly the YOLO (You Only Look Once) algorithm, has gained significant attention for its real-time performance and accuracy. YOLO is a powerful object detection model that processes images in a single pass, making it highly efficient for identifying potholes in roadways. By training the model on annotated datasets of road images, YOLO can detect potholes with high precision, even under varying lighting and environmental conditions. This technology helps in proactive road maintenance by alerting authorities to damaged road surfaces before they worsen. The implementation of YOLO for pothole detection involves collecting and preprocessing image data, annotating potholes, and training the model using convolutional neural networks (CNNs). Once trained, the model can be integrated into dashcam systems or drones to scan roads in real-time. Compared to traditional methods, YOLO offers faster detection speeds and higher accuracy, making it ideal for large-scale deployment. Future advancements in deep learning and dataset diversity will further enhance pothole detection systems, leading to safer and well-maintained roads.