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Article

Deep Learning-Based Feature Silencing for Accurate Concrete Crack Detection

Department of Computer Science and Engineering, University of Nevada, Reno, NV 89557, USA
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Sensors 2020, 20(16), 4403; https://doi.org/10.3390/s20164403
Received: 20 July 2020 / Revised: 2 August 2020 / Accepted: 5 August 2020 / Published: 7 August 2020
(This article belongs to the Special Issue Computer Vision for Remote Sensing and Infrastructure Inspection)
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. View Full-Text
Keywords: convolutional neural network; encoder-decoder architecture; semantic segmentation; feature silencing; crack detection convolutional neural network; encoder-decoder architecture; semantic segmentation; feature silencing; crack detection
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MDPI and ACS Style

Billah, U.H.; La, H.M.; Tavakkoli, A. Deep Learning-Based Feature Silencing for Accurate Concrete Crack Detection. Sensors 2020, 20, 4403. https://doi.org/10.3390/s20164403

AMA Style

Billah UH, La HM, Tavakkoli A. Deep Learning-Based Feature Silencing for Accurate Concrete Crack Detection. Sensors. 2020; 20(16):4403. https://doi.org/10.3390/s20164403

Chicago/Turabian Style

Billah, Umme H., Hung M. La, and Alireza Tavakkoli. 2020. "Deep Learning-Based Feature Silencing for Accurate Concrete Crack Detection" Sensors 20, no. 16: 4403. https://doi.org/10.3390/s20164403

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