Citation: Tan, H.; Dong, S.
Pixel-Level Concrete Crack
Segmentation Using Pyramidal
Residual Network with
Omni-Dimensional Dynamic
Convolution. Processes 2023, 11, 546.
https://doi.org/10.3390/pr11020546
Academic Editors: Kelvin K.L. Wong,
Dhanjoo N. Ghista, Andrew W.H. Ip
and Wenjun (Chris) Zhang
Received: 5 January 2023
Revised: 28 January 2023
Accepted: 8 February 2023
Published: 10 February 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Article
Pixel-Level Concrete Crack Segmentation Using
Pyramidal Residual Network with Omni-Dimensional
Dynamic Convolution
Hao Tan and Shaojiang Dong *
College of Mechatronics and Automotive Engineering, Chongqing Jiaotong University, Chongqing 400074, China
* Correspondence: dongshaojiang100@163.com
Abstract:
Automated crack detection technologies based on deep learning have been extensively
used as one of the indicators of performance degradation of concrete structures. However, there are
numerous drawbacks of existing methods in crack segmentation due to the fine and microscopic
properties of cracks. Aiming to address this issue, a crack segmentation method is proposed. First, a
pyramidal residual network based on encoder–decoder using Omni-Dimensional Dynamic Convolu-
tion is suggested to explore the network suitable for the task of crack segmentation. Additionally,
the proposed method uses the mean intersection over union as the network evaluation index to
lessen the impact of background features on the network performance in the evaluation and adopts
a multi-loss calculation of positive and negative sample imbalance to weigh the negative impact
of sample imbalance. As a final step in performance evaluation, a dataset for concrete cracks is
developed. By using our dataset, the proposed method is validated to have an accuracy of 99.05%
and an mIoU of 87.00%. The experimental results demonstrate that the concrete crack segmentation
method is superior to the well-known networks, such as SegNet, DeeplabV3+, and Swin-unet.
Keywords:
concrete crack; image segmentation; omni-dimensional dynamic convolution; pyramidal
residual network; unbalanced sample
1. Introduction
Cracks in concrete are considered as a significant flaw when inspecting civil engineer-
ing projects. From an engineering perspective, fractures affect not only the stability and
longevity of engineering constructions, but also the durability of concrete [
1
], which may
be caused by small or massive cracks that slowly spread and cause the final collapse or
destruction of the structure. Currently, the primary approaches to detect crack-like flaws
refer to simple instrumental measurements and visual inspection. The latter, however, is
considered as an arduous operation. Moreover, there may be significant misdetection and
omission in some regions with problems [
2
]. Manual crack detection is not ideal for mass
detection since it frequently encounters problems such as heavy workload, complex struc-
ture, and inconsistent evaluation standards. Compared with manual inspection, machine
vision inspection shows the features of efficiency as well as safety and reliability due to its
lack of contact with the object. Traditional machine vision methods have been extensively
used to solve industrial problems, including object inspection [
3
], material contour measure-
ment [
4
], distance measurement [
5
], etc. For example, multi-vision measurement methods
can be used to accurately measure the surface deformation and full-field strain values
of steel pipe concrete columns [
6
]. The use of exponential functional density clustering
models can perform better than the clustering and deep learning (DL) methods for indoor
object extraction tasks [
7
]. Despite the considerable achievements, conventional vision
technologies still require expert analysis and fine-tuning for their application, making them
inappropriate for complex problems. Due to the continuous innovation and development
of digital images, the combination of digital image processing methods and DL in the
Processes 2023, 11, 546. https://doi.org/10.3390/pr11020546 https://www.mdpi.com/journal/processes
