Review of Pavement Defect Detection Methods

Wenming Cao,Zhiquan He,Qifan Liu

doi:10.1109/access.2020.2966881

Wenming Cao, Zhiquan He + Show 1 more

Open Access

https://doi.org/10.1109/access.2020.2966881

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Abstract

Road pavement cracks detection has been a hot research topic for quite a long time due to the practical importance of crack detection for road maintenance and traffic safety. Many methods have been proposed to solve this problem. This paper reviews the three major types of methods used in road cracks detection: image processing, machine learning and 3D imaging based methods. Image processing algorithms mainly include threshold segmentation, edge detection and region growing methods, which are used to process images and identify crack features. Crack detection based traditional machine learning methods such as neural network and support vector machine still relies on hand-crafted features using image processing techniques. Deep learning methods have fundamentally changed the way of crack detection and greatly improved the detection performance. In this work, we review and compare the deep learning neural networks proposed in crack detection in three ways, classification based, object detection based and segmentation based. We also cover the performance evaluation metrics and the performance of these methods on commonly-used benchmark datasets. With the maturity of 3D technology, crack detection using 3D data is a new line of research and application. We compare the three types of 3D data representations and study the corresponding performance of the deep neural networks for 3D object detection. Traditional and deep learning based crack detection methods using 3D data are also reviewed in detail.

Highlights

With the rapid development of road traffic, people have paid more and more attention to the importance of pavement maintenance as road surface cracks affect the transportation efficiency and pose a potential threat to vehicle safety
We provide a comprehensive review of pavement crack detection methods, especially the in-depth analysis of deep learning and 3D image based methods
Methods based on object detection like SSD and faster R-CNN propose multiple candidate regions and perform the location regression using the image features extracted from CNN structure is a systematic way for object detection

Summary

INTRODUCTION

With the rapid development of road traffic, people have paid more and more attention to the importance of pavement maintenance as road surface cracks affect the transportation efficiency and pose a potential threat to vehicle safety. In [16] Mathavan et al discussed the detection of road surface lesions from the perspective of 3D image defect detection, summarized the application of 3D imaging technologies in road surface monitoring, analyzed the imaging principle of different devices and compared the advantages and disadvantages of different pavement detection technologies. These reviews address different emphasis or aspect on road surface detection.

CRACK DETECTION BASED ON IMAGE PROCESSING

EXISTING PROBLEMS AND RESEARCH PROSPECTS

Findings

CONCLUSION