Review of Pavement Defect Detection Methods

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. INDEX TERMS Crack detection, image processing, deep learning, 3D imaging.

LMDAPNet: A Novel Manifold-Based Deep Learning Network

“…As can be seen from the figure, we notice that the block size has a huge impact on recognition accuracy. All cases the recognition rate increase except for Extended Yale B when [b 1 ,b 2 ]< [7,7], achieving the best performance when [b 1 ,b 2 ]= [7,7]. Also, the result suggests LMDAPNet with smaller block size on the occlusion test set of AR dataset achieves better results.…”

“…4 and Fig. 5, we briefly summarize the appropriate setting of the parameters for our LMDAPNet as follows: [b 1 ,b 2 ]= [7,7], while should be adapted according to the image size, and our experiences also show that it performs reasonably well when α = 0.4 in most cases.…”

“…In recent years, feature extraction methods based on deep architecture have achieved great success in a series of tasks [6], [7], [8]. Therefore, a large number of researchers have turned their perspective to the method of feature extraction based on deep architecture [9], [10].…”

Lightweight Semantic Segmentation for Road-Surface Damage Recognition Based on Multiscale Learning

“…For this experimental comparison, 9,000 images were taken with a smart phone and 15,000 objects were marked. The comparison showed that MobileNet utilizes 30.6 ms but did not work properly in detecting long and curved shapes such as cracks because this object detection algorithm is suitable for recognizing objects such as vehicles or people [21].…”

Improved Pixel-Level Pavement-Defect Segmentation Using a Deep Autoencoder

“…The authors in [1] reviewed DL network application publications on pavement crack detection since its first appearance in 2016. The importance of the matter can be seen from a recent review paper on pavement defect detection methods [4]. The number of reviewed methods/publications exceeds 100, and half of them are not older than five years.…”