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An apposite transfer-learned DCNN model for prediction of structural surface cracks under optimal threshold for class-imbalanced data

An apposite transfer-learned DCNN model for prediction of structural surface cracks under optimal... Due to stochastic occurrence of surface defects in a structure, size of acquired image datasets may vary for cracked and un-cracked classes. Further, in crack detection and classification, among misclassified predictions, while, false-positives can be particularly important that can provide added safety factor to the structural health monitoring system to adopt early preventive measures, false negatives can result in an overconfident health monitoring system thereby seriously affecting the durability of a structure. In this study, the authors aimed to address these two problems, by transfer learning five pre-trained deep convolution neural network (DCNN) models on the same target dataset using binary focal loss and evaluated the models’ performance in comparison to the binary cross-entropy loss function. Five model sets each consisting twenty four variations have been generated by varying the dropout and loss function parameters, from which the best performing model has been proposed. The influence of the focussing parameter, γ on the model accuracy has also been investigated. Finally, three independent test datasets are used to evaluate the generalization capacity of the proposed model under optimal thresholds which yielded in appreciable metrics outcome. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Building Pathology and Rehabilitation Springer Journals

An apposite transfer-learned DCNN model for prediction of structural surface cracks under optimal threshold for class-imbalanced data

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References (41)

Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
ISSN
2365-3159
eISSN
2365-3167
DOI
10.1007/s41024-022-00226-6
Publisher site
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Abstract

Due to stochastic occurrence of surface defects in a structure, size of acquired image datasets may vary for cracked and un-cracked classes. Further, in crack detection and classification, among misclassified predictions, while, false-positives can be particularly important that can provide added safety factor to the structural health monitoring system to adopt early preventive measures, false negatives can result in an overconfident health monitoring system thereby seriously affecting the durability of a structure. In this study, the authors aimed to address these two problems, by transfer learning five pre-trained deep convolution neural network (DCNN) models on the same target dataset using binary focal loss and evaluated the models’ performance in comparison to the binary cross-entropy loss function. Five model sets each consisting twenty four variations have been generated by varying the dropout and loss function parameters, from which the best performing model has been proposed. The influence of the focussing parameter, γ on the model accuracy has also been investigated. Finally, three independent test datasets are used to evaluate the generalization capacity of the proposed model under optimal thresholds which yielded in appreciable metrics outcome.

Journal

Journal of Building Pathology and Rehabilitation – Springer Journals

Published: Dec 1, 2022

Keywords: Structural concrete crack; Image processing; Binary classification; Computer vision; Deep learning; Convolution neural network

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