Abstract: Our study addresses the critical need for innovation in transportation infrastructure management, particularly in the realm of pothole detection. Traditional methods are time- consuming and resource-intensive, prompting us to explore advanced technologies like Convolutional Neural Networks (CNN), You Only Look Once (YOLO), transfer learning, and OpenCV-based methodologies. Our goal is to stream- line pothole detection, allowing transportation authorities to allocate resources more effectively for infrastructure main- tenance. Through a comprehensive analysis, our objective is to identify the strengths and weaknesses of each technique, aiding decision-making in selecting and implementing pothole detection strategies. This interdisciplinary approach has the potential to revolutionize the management of potholes, resulting in improved efficiency, objectivity, and consistency in road maintenance practices.

Index Terms: pothole detection, Convolutional Neural Net- works (CNN), You Only Look Once (YOLO), transfer learning, Support Vector Machines (SVMs), Random Forests.

| DOI: 10.17148/IARJSET.2024.11663