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Deep Learning-Based Feature Silencing for Accurate Concrete Crack Detection
An autonomous concrete crack inspection system is necessary for preventing hazardous incidents arising from deteriorated concrete surfaces. In this paper, we present a concrete crack detection framework to aid the process of automated inspection. The proposed approach employs a deep convolutional ne...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7472489/ https://www.ncbi.nlm.nih.gov/pubmed/32784557 http://dx.doi.org/10.3390/s20164403 |
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author | Billah, Umme Hafsa La, Hung Manh Tavakkoli, Alireza |
author_facet | Billah, Umme Hafsa La, Hung Manh Tavakkoli, Alireza |
author_sort | Billah, Umme Hafsa |
collection | PubMed |
description | An autonomous concrete crack inspection system is necessary for preventing hazardous incidents arising from deteriorated concrete surfaces. In this paper, we present a concrete crack detection framework to aid the process of automated inspection. The proposed approach employs a deep convolutional neural network architecture for crack segmentation, while addressing the effect of gradient vanishing problem. A feature silencing module is incorporated in the proposed framework, capable of eliminating non-discriminative feature maps from the network to improve performance. Experimental results support the benefit of incorporating feature silencing within a convolutional neural network architecture for improving the network’s robustness, sensitivity, and specificity. An added benefit of the proposed architecture is its ability to accommodate for the trade-off between specificity (positive class detection accuracy) and sensitivity (negative class detection accuracy) with respect to the target application. Furthermore, the proposed framework achieves a high precision rate and processing time than the state-of-the-art crack detection architectures. |
format | Online Article Text |
id | pubmed-7472489 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-74724892020-09-17 Deep Learning-Based Feature Silencing for Accurate Concrete Crack Detection Billah, Umme Hafsa La, Hung Manh Tavakkoli, Alireza Sensors (Basel) Article An autonomous concrete crack inspection system is necessary for preventing hazardous incidents arising from deteriorated concrete surfaces. In this paper, we present a concrete crack detection framework to aid the process of automated inspection. The proposed approach employs a deep convolutional neural network architecture for crack segmentation, while addressing the effect of gradient vanishing problem. A feature silencing module is incorporated in the proposed framework, capable of eliminating non-discriminative feature maps from the network to improve performance. Experimental results support the benefit of incorporating feature silencing within a convolutional neural network architecture for improving the network’s robustness, sensitivity, and specificity. An added benefit of the proposed architecture is its ability to accommodate for the trade-off between specificity (positive class detection accuracy) and sensitivity (negative class detection accuracy) with respect to the target application. Furthermore, the proposed framework achieves a high precision rate and processing time than the state-of-the-art crack detection architectures. MDPI 2020-08-07 /pmc/articles/PMC7472489/ /pubmed/32784557 http://dx.doi.org/10.3390/s20164403 Text en © 2020 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Billah, Umme Hafsa La, Hung Manh Tavakkoli, Alireza Deep Learning-Based Feature Silencing for Accurate Concrete Crack Detection |
title | Deep Learning-Based Feature Silencing for Accurate Concrete Crack Detection |
title_full | Deep Learning-Based Feature Silencing for Accurate Concrete Crack Detection |
title_fullStr | Deep Learning-Based Feature Silencing for Accurate Concrete Crack Detection |
title_full_unstemmed | Deep Learning-Based Feature Silencing for Accurate Concrete Crack Detection |
title_short | Deep Learning-Based Feature Silencing for Accurate Concrete Crack Detection |
title_sort | deep learning-based feature silencing for accurate concrete crack detection |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7472489/ https://www.ncbi.nlm.nih.gov/pubmed/32784557 http://dx.doi.org/10.3390/s20164403 |
work_keys_str_mv | AT billahummehafsa deeplearningbasedfeaturesilencingforaccurateconcretecrackdetection AT lahungmanh deeplearningbasedfeaturesilencingforaccurateconcretecrackdetection AT tavakkolialireza deeplearningbasedfeaturesilencingforaccurateconcretecrackdetection |