Computer Vision for Road Imaging and Pothole Detection: A State-of-the-Art Review of Systems and Algorithms

TL;DR

This survey reviews state-of-the-art computer vision techniques, especially deep learning, for 3-D road imaging and pothole detection, highlighting recent advances, challenges, and future trends in the field.

Contribution

It provides a comprehensive overview of sensing systems and algorithms, emphasizing the shift from classical methods to CNN-based approaches for pothole detection.

Findings

Deep learning, especially CNNs, outperform classical methods in pothole detection.

3-D point cloud modeling and segmentation are becoming obsolete.

Future trends include self-supervised learning for improved accuracy.

Abstract

Computer vision algorithms have been prevalently utilized for 3-D road imaging and pothole detection for over two decades. Nonetheless, there is a lack of systematic survey articles on state-of-the-art (SoTA) computer vision techniques, especially deep learning models, developed to tackle these problems. This article first introduces the sensing systems employed for 2-D and 3-D road data acquisition, including camera(s), laser scanners, and Microsoft Kinect. Afterward, it thoroughly and comprehensively reviews the SoTA computer vision algorithms, including (1) classical 2-D image processing, (2) 3-D point cloud modeling and segmentation, and (3) machine/deep learning, developed for road pothole detection. This article also discusses the existing challenges and future development trends of computer vision-based road pothole detection approaches: classical 2-D image processing-based and 3-D point cloud modeling and segmentation-based approaches have already become history; and Convolutional neural networks (CNNs) have demonstrated compelling road pothole detection results and are promising to break the bottleneck with the future advances in self/un-supervised learning for multi-modal semantic segmentation. We believe that this survey can serve as practical guidance for developing the next-generation road condition assessment systems.