Automatic Crack Detection on Road Pavements Using Encoder-Decoder Architecture

Abstract

<jats:p>Automatic crack detection from images is an important task that is adopted to ensure road safety and durability for Portland cement concrete (PCC) and asphalt concrete (AC) pavement. Pavement failure depends on a number of causes including water intrusion, stress from heavy loads, and all the climate effects. Generally, cracks are the first distress that arises on road surfaces and proper monitoring and maintenance to prevent cracks from spreading or forming is important. Conventional algorithms to identify cracks on road pavements are extremely time-consuming and high cost. Many cracks show complicated topological structures, oil stains, poor continuity, and low contrast, which are difficult for defining crack features. Therefore, the automated crack detection algorithm is a key tool to improve the results. Inspired by the development of deep learning in computer vision and object detection, the proposed algorithm considers an encoder-decoder architecture with hierarchical feature learning and dilated convolution, named U-Hierarchical Dilated Network (U-HDN), to perform crack detection in an end-to-end method. Crack characteristics with multiple context information are automatically able to learn and perform end-to-end crack detection. Then, a multi-dilation module embedded in an encoder-decoder architecture is proposed. The crack features of multiple context sizes can be integrated into the multi-dilation module by dilation convolution with different dilatation rates, which can obtain much more cracks information. Finally, the hierarchical feature learning module is designed to obtain a multi-scale features from the high to low- level convolutional layers, which are integrated to predict pixel-wise crack detection. Some experiments on public crack databases using 118 images were performed and the results were compared with those obtained with other methods on the same images. The results show that the proposed U-HDN method achieves high performance because it can extract and fuse different context sizes and different levels of feature maps than other algorithms.</jats:p>

A novel pavement transverse cracks detection model using WT-CNN and STFT-CNN for smartphone data analysis

“…The proposed U-HDN method can fuse feature maps of different context sizes and different levels. Fan et al (2020b) demonstrated the use of DCNNs to detect and identify cracks as crack defects with quantified attribute applications for detecting pavement surfaces. Smartphones have a built-in accelerometer, gyroscopes, and GPS (Global Positioning System), allowing to collect acceleration data in real time and estimate a wide range of road conditions with lots of data.…”

DAUNet: Deep Augmented Neural Network for Pavement Crack Segmentation

“…That is why it has attracted much attention for scientific research. Fan et al [1] and Cao et al [2] have conducted an extensive survey of the various crack detection algorithms. Among those in recent years, deep learning has been widely applied to crack detection, because of its excellent robust feature representation capability.…”

Jointed Plain Concrete Pavements in Airports: Structural–Economic Evaluation and Proposal for a Catalogue

“…The large experience of the IAF has helped to collect data on design, construction, and maintenance of runways over more than 80 years. With regard to concrete pavements, Jointed Plain Concrete Pavements (JPCP) without dowel and tie bars are the most frequent solution because of simple and rapid execution, lower cost than other concrete solutions (the presence of bars involves an increase in expenditure varying from 8% to 20%, while a continuous reinforcement involves a higher initial expense of 50% or more) [6], good monitored performances [7][8][9][10][11], and simple maintenance works [12,13], the latter with potential applications to other facilities, where regular maintenance might lag behind [14][15][16]. The absence of reinforcement requires a double foundation layer: a bottom one composed of a granular mixture and a top one composed of a cement-stabilized mixture.…”