Comparison of CNN-Based Models for Pothole Detection in Real-World Adverse Conditions: Overview and Evaluation

Abstract

Potholes pose a significant problem for road safety and infrastructure. They can cause damage to vehicles and present a risk to pedestrians and cyclists. The ability to detect potholes in real time and with a high level of accuracy, especially under different lighting conditions, is crucial for the safety of road transport participants and the timely repair of these hazards. With the increasing availability of cameras on vehicles and smartphones, there is a growing interest in using computer vision techniques for this task. Convolutional neural networks (CNNs) have shown great potential for object detection tasks, including pothole detection. This study provides an overview of computer vision algorithms used for pothole detection. Experimental results are then used to evaluate the performance of the latest CNN-based models for pothole detection in different real-world road conditions, including rain, sunset, evening, and night, as well as clean conditions. The models evaluated in this study include both conventional and the newest architectures from the region-based CNN (R-CNN) and You Only Look Once (YOLO) families. The YOLO models demonstrated a faster detection response and higher accuracy in detecting potholes under clear, rain, sunset, and evening conditions. R-CNN models, on the other hand, performed better in the worse-visibility conditions at night. This study provides valuable insights into the performance of different CNN models for pothole detection in real road conditions and may assist in the selection of the most appropriate model for a specific application.