Autonomous Concrete Crack Semantic Segmentation Using Deep Fully Convolutional Encoder–Decoder Network in Concrete Structures Inspection

13Citations
Citations of this article
35Readers
Mendeley users who have this article in their library.

Abstract

Structure health inspection is the way to ensure that structures stay in optimum condition. Traditional inspection work has many disadvantages in dealing with the large workload despite using remote image-capturing devices. This research focuses on image-based concrete crack pattern recognition utilizing a deep convolutional neural network (DCNN) and an encoder–decoder module for semantic segmentation and classification tasks, thereby lightening the inspectors’ workload. To achieve this, a series of contrast experiments have been implemented. The results show that the proposed deep-learning network has competitive semantic segmentation accuracy (91.62%) and over-performs compared with other crack detection studies. This proposed advanced DCNN is split into multiple modules, including atrous convolution (AS), atrous spatial pyramid pooling (ASPP), a modified encoder–decoder module, and depthwise separable convolution (DSC). The advancement is that those modules are well-selected for this task and modified based on their characteristics and functions, exploiting their superiority to achieve robust and accurate detection globally. This application improved the overall performance of detection and can be implemented in industrial practices.

References Powered by Scopus

Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

9638Citations
N/AReaders
Get full text

Road crack detection using deep convolutional neural network

1370Citations
N/AReaders
Get full text

Autonomous Structural Visual Inspection Using Region-Based Deep Learning for Detecting Multiple Damage Types

1295Citations
N/AReaders
Get full text

Cited by Powered by Scopus

Visual Detection of Road Cracks for Autonomous Vehicles Based on Deep Learning

9Citations
N/AReaders
Get full text

Material-Aware Path Aggregation Network and Shape Decoupled SIoU for X-ray Contraband Detection

8Citations
N/AReaders
Get full text

A dynamic semantic segmentation algorithm with encoder-crossor-decoder structure for pixel-level building cracks

6Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Pu, R., Ren, G., Li, H., Jiang, W., Zhang, J., & Qin, H. (2022). Autonomous Concrete Crack Semantic Segmentation Using Deep Fully Convolutional Encoder–Decoder Network in Concrete Structures Inspection. Buildings, 12(11). https://doi.org/10.3390/buildings12112019

Readers over time

‘23‘24‘2506121824

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 7

64%

Lecturer / Post doc 2

18%

Researcher 2

18%

Readers' Discipline

Tooltip

Engineering 10

77%

Computer Science 2

15%

Earth and Planetary Sciences 1

8%

Save time finding and organizing research with Mendeley

Sign up for free
0