Sample and Structure-Guided Network for Road Crack Detection

Abstract

As an indispensable task for traffic management department, road maintenance has attracted much attention during the last decade due to the rapid development of traffic network. As is known, crack is the early form of many road damages, and repair it in time can significantly save the maintenance cost. In this case, how to detect crack regions quickly and accurately becomes a huge demand. Actually, many image processing technique based methods have been proposed for crack detection, but their performances can not meet our expectations. The reason is that, most of these methods use bottom features such as color and texture to detect the cracks, which are easily influenced by the varied conditions such as light and shadow. Inspired by the great successes of machine learning and artificial intelligence, this paper presents a sample and structure guided network for detecting road cracks. Specifically, the proposed network is based on U-Net architecture, which remains the details from input to output by using skip connection strategy. Then, because the scale of crack samples is much smaller than that of non-crack ones, directly using the conventional cross entropy loss can not optimize the network effectively. In this case, the Focal loss is utilized to address the model optimization problem. Additionally, we incorporate the self-attention strategy into the proposed network, which enhances its stability by encoding the 2-order information among different local regions into the final features. Finally, we test the proposed method on four datasets, three public ones with labels and a photographed one without labels, to validate its effectiveness. It is noteworthy that, for the photographed dataset, we design a series of image processing strategies such as contrast enhancement to improve the generalization capability of the proposed method.

Road crack detection using convolutional neural network

“…Additionally, they proposed a series of image enhancement strategies to generalize the proposed method to other open datasets, which improves its application value to an outsized extent. Finally, experimental results on three public and a photographed datasets validate the robustness, effectiveness and superiority of the proposed algorithm (30) .…”

Bibliometric Analysis and Review of Deep Learning-Based Crack Detection Literature Published between 2010 and 2022

“…The overall distribution of the articles is shown in Figure 9 and the articles are shown in Table 10. [24,35,83,97,98,101,164,168,170,188,197,200,[205][206][207][208][209]…”

Multitask Learning Mechanism for Remote Sensing Image Motion Deblurring

“…In these cases, a structure-preserving network is very important for images deblurring, which must preserve sufficient detailed texture information from the original image to predicted image. Recently, U-Net have shown satisfactory performance in this point because of the skip connection mechanism [51], [52], and hence we use it as the backbone network of our restoration branch.…”