Autonomous concrete crack semantic segmentation using deep fully convolutional encoder-decoder network in concrete structures inspection

Pu, Rundong, Ren, Guoqian, Li, Haijiang ORCID: https://orcid.org/0000-0001-6326-8133, Jiang, Wei, Zhang, Jisong and Qin, Honglei 2022. Autonomous concrete crack semantic segmentation using deep fully convolutional encoder-decoder network in concrete structures inspection. Buildings 12 (11) , 2019. 10.3390/buildings12112019

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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.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Engineering
ISSN:	0007-3725
Date of First Compliant Deposit:	18 November 2022
Date of Acceptance:	16 November 2022
Last Modified:	13 May 2023 19:48
URI:	https://orca.cardiff.ac.uk/id/eprint/154321

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