
 Computer vision algorithms have been 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. It then comprehensively reviews the SoTA computer vision algorithms, including (1) classical 2-D image processing, (2) 3-D point cloud modelling and segmentation and (3) machine/deep learning, developed for road pothole detection. The 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 modelling 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 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.

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

Road unevenness is a significant factor affecting the comprehensive driving performance of vehicles. Monitoring road roughness conditions in advance significantly contributes to ride safety and comfort. However, there are hardly comprehensive implementations for comfort promotion purpose based on LiDAR point clouds. This paper proposes a hierarchical scheme that separately describes road regular and irregular unevenness. Firstly, real-vehicle experiments for collecting point clouds of various uneven roads are conducted with the designed hardware platform. A dense point cloud dataset with road bump or pothole annotations is released. Then, an improved PointNet++ model adapted to irregular unevenness recognition is developed. Comparison experiments with the recent segmentation methods verify the superiority of our model in this task. The mIoU metric on the test set reaches 91.6%, outperforming 3.0% than the original model. Road elevation profiles on tire trajectories are finally constructed. The statistical parameters of regular unevenness and geometric features of the irregular sections are extracted, respectively. The proposed perception scheme for road roughness provides detailed and accurate road condition information. It has great potential in practical applications for road unevenness monitoring and vehicle comfort improvement.

A Hierarchical Scheme of Road Unevenness Perception with LiDAR for Autonomous Driving Comfort

Potholes and other road surface damages pose significant risks to vehicles and traffic safety. The current methods of in situ visual inspection for potholes or cracks are inefficient, costly, and hazardous. Therefore, there is a pressing need to develop automated systems for assessing road surface conditions, aiming to efficiently and accurately reconstruct, recognize, and locate potholes. In recent years, various methods utilizing (a) computer vision, (b) three-dimensional (3D) point clouds, or (c) smartphone data have been employed to map road surface quality conditions. Machine learning and deep learning techniques have increasingly enhanced the performance of these methods. This review aims to provide a comprehensive overview of cutting-edge computer vision and machine learning algorithms for pothole detection. It covers topics such as sensing systems for acquiring two-dimensional (2D) and 3D road data, classical algorithms based on 2D image processing, segmentation-based algorithms using 3D point cloud modeling, machine learning, deep learning algorithms, and hybrid approaches. The review highlights that hybrid methods combining traditional image processing and advanced machine learning techniques offer the highest accuracy in pothole detection. Machine learning approaches, particularly deep learning, demonstrate superior adaptability and detection rates, while traditional 2D and 3D methods provide valuable baseline techniques. By reviewing and evaluating existing vision-based methods, this paper clarifies the current landscape of pothole detection technologies and identifies opportunities for future research and development. Additionally, insights provided by this review can inform the design and implementation of more robust and effective systems for automated road surface condition assessment, thereby contributing to enhanced roadway safety and infrastructure management. 

A Review of Vision-Based Pothole Detection Methods Using Computer Vision and Machine Learning

Ensuring traffic safety is crucial, which necessitates the detection and prevention of road surface defects. As a result, there has been a growing interest in the literature on the subject, leading to the development of various road surface defect detection methods. The methods for detecting road defects can be categorised in various ways depending on the input data types or training methodologies. The predominant approach involves image-based methods, which analyse pixel intensities and surface textures to identify defects. Despite their popularity, image-based methods share the distinct limitation of vulnerability to weather and lighting changes. To address this issue, researchers have explored the use of additional sensors, such as laser scanners or LiDARs, providing explicit depth information to enable the detection of defects in terms of scale and volume. However, the exploration of data beyond images has not been sufficiently investigated. In this survey paper, we provide a comprehensive review of road surface defect detection studies, categorising them based on input data types and methodologies used. Additionally, we review recently proposed non-image-based methods and discuss several challenges and open problems associated with these techniques.

Road Surface Defect Detection - From Image-based to Non-image-based: A Survey

The extraction of pavement damage information is one of the major difficulties in the application research of mobile laser scanning point cloud data. To address the problem of inaccurate detection results by using only relative distance to detect potholes, this paper proposes a novel pothole detection method that combines directed distance and skewed distribution. Firstly, the rapid positioning of the pothole is realized by the directed distance, which is calculated from the points and the local fitted plane. And monomerization and denoising of potential potholes are achieved by density clustering. Then, the new accurate plane is fitted by the surrounding pavement points of the potential pothole to obtain accurate directed distances. The negative skewed distribution of the directed distance histogram and the skewness coefficient are used for the accurate determination of the pothole. Finally, the three-dimensional geometric features of the pothole are extracted. Experiments were carried out on a road with poor road conditions. The experimental results validated the effectiveness and practicality of the proposed method. It can achieve automatic detection of potholes with different shapes and deformation degrees, and has effectively improved the efficiency of automatic road inspection.

Road potholes detection from MLS point clouds

Road potholes affect comfort, safety, traffic condition and vehicle stability. Accurately detecting these potholes is vital for assessing the degree of pavement distress and developing road maintenance plan accordingly. This paper proposes a simple and effective pothole detection method based on 3D point cloud segmentation. Using binocular stereo vision to acquire 3D point clouds, fitting the pavement plane and then eliminating it from the 3D point clouds of road scene, we could roughly extract the pothole. K-means clustering and region growing algorithms were adopted to extract the potholes precisely. The experimental results demonstrate that our proposed method has a very good segmentation effect on scenes involving plane and target object.

A pothole detection method based on 3D point cloud segmentation

Despite its rapid advancement in the past two decades, bidimensional empirical mode decomposition (BEMD) still has several limitations in multi-scale feature description of input images. To ameliorate this issue, in this paper we present several optimization-based approaches to BEMD. First, we articulate an improved unconstrained optimization approach to BEMD (IUOA-BEMD). The essential idea is to formulate an optimization model to decompose an input image based on the Delaunay triangulation of its local maxima (minima). Second, we design a scale-guided optimization approach to BEMD (SGO-BEMD) so as to arrive at an improved modal image. SGO-BEMD uses the initial modal image (obtained from the aforementioned proposed IUOA-BEMD) as a necessary guide and can capture much clearer features at various spatial scales of the input image. In addition, an additional edge-preserving property can be obtained with the edge-aware decomposition if an edge-aware scale-guided optimization to BEMD (EASGO-BEMD) is used. The visualization and quantitative results for many artificial amplitude-modulated–frequency-modulated (AM-FM) images and real images have shown that the newly-proposed methods are very competitive with state-of-the-art BEMD methods. Moreover, we further evaluate the performance of BEMD methods according to their applications in image detail enhancement and image contrast & brightness enhancement. It may be noted that image contrast & brightness enhancement represents the first attempt to integrate BEMD with Retinex theory. Collectively, both types of enhancement validate the utility of the novel optimization-based approaches to BEMD proposed herein. 

Novel optimization-based bidimensional empirical mode decomposition

In the daily driving process, road potholes pose a great threat to traffic safety. However, the actual road potholes are often irregular and the background is complex. So, it is difficult to accurately measure the volume of potholes. In this paper, a method for measuring the volume of road potholes based on three-dimensional point clouds is presented. First, binocular vision is used to obtain 3D point cloud data of the pothole, and the segmented pothole point clouds are projected onto the coordinate plane established by the road surface. Then, we triangulate projection points on the coordinate plane. Finally, restore these points to the true elevation points to generate triangular prism, and calculate the volume of a single triangular prism one by one. The accuracy and effectiveness of this algorithm are verified by experiments.

Accurate volume measurement of road potholes based on 3D point clouds

Fusion of infrared and visible images via structure and texture-aware retinex

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A pothole detection method based on 3D point cloud segmentation

Novel optimization-based bidimensional empirical mode decomposition

Accurate volume measurement of road potholes based on 3D point clouds

Fusion of infrared and visible images via structure and texture-aware retinex