Automated Pavement Crack Damage Detection Using Deep Multiscale Convolutional Features

Weidong Song,Di Jia,Hong Zhu,Guohui Jia,Lin Gao

doi:10.1155/2020/6412562

Abstract

Road pavement cracks automated detection is one of the key factors to evaluate the road distress quality, and it is a difficult issue for the construction of intelligent maintenance systems. However, pavement cracks automated detection has been a challenging task, including strong nonuniformity, complex topology, and strong noise-like problems in the crack images, and so on. To address these challenges, we propose the CrackSeg—an end-to-end trainable deep convolutional neural network for pavement crack detection, which is effective in achieving pixel-level, and automated detection via high-level features. In this work, we introduce a novel multiscale dilated convolutional module that can learn rich deep convolutional features, making the crack features acquired under a complex background more discriminant. Moreover, in the upsampling module process, the high spatial resolution features of the shallow network are fused to obtain more refined pixel-level pavement crack detection results. We train and evaluate the CrackSeg net on our CrackDataset, the experimental results prove that the CrackSeg achieves high performance with a precision of 98.00%, recall of 97.85%, F-score of 97.92%, and a mIoU of 73.53%. Compared with other state-of-the-art methods, the CrackSeg performs more efficiently, and robustly for automated pavement crack detection.

Highlights

Pavement crack detection plays an important role in the eld of road distress evaluation [1]
In most cases, there is a considerable noise in cracks, which leads to poor continuity and low contrast, as shown in Figure 1(b). erefore, automatic crack detection mainly includes the following three challenges. (i) In a poorly lit environment and complex background, the texture, and linearity of interference have similar features, resulting in greater intraclass di erences. (ii) Boundary blurring occurs between small cracks and local noises. (iii) Blurred low-quality images from crack data collected at high speed are unavoidable. ese three di culties create considerable challenges in pavement crack detection
The shallow and deep semantic information is fused by the upsampling module so that the network output feature mapping size is consistent with the input image size, and the probability that each pixel belongs to cracks or noncracks is calculated by the so max function. e vector value [0, 1] generated by the so max function represents the probability distribution of a class, and the so max function can be expressed as:

Summary

Introduction

Pavement crack detection plays an important role in the eld of road distress evaluation [1]. Is procedure is a key part of intelligent maintenance systems, to assist and evaluate the pavement distress quality where more continual road status surveys are required. Many methods utilize computer vision algorithms to process the collected pavement crack images and obtain the nal maintenance evaluation results [4]. Erefore, automatic crack detection mainly includes the following three challenges. (iii) Blurred low-quality images from crack data collected at high speed are unavoidable. In most cases, there is a considerable noise in cracks, which leads to poor continuity and low contrast, as shown in Figure 1(b). erefore, automatic crack detection mainly includes the following three challenges. (i) In a poorly lit environment and complex background, the texture, and linearity of interference (weeds, stains, etc.) have similar features, resulting in greater intraclass di erences. (ii) Boundary blurring occurs between small cracks and local noises. (iii) Blurred low-quality images from crack data collected at high speed are unavoidable. ese three di culties create considerable challenges in pavement crack detection

Methods

Discussion

Conclusion