Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Deep Learning Based Feature Silencing for Accurate Concrete Crack Detection

Version 1 : Received: 20 July 2020 / Approved: 21 July 2020 / Online: 21 July 2020 (13:54:13 CEST)

A peer-reviewed article of this Preprint also exists.

Billah, U.H.; La, H.M.; Tavakkoli, A. Deep Learning-Based Feature Silencing for Accurate Concrete Crack Detection. Sensors 2020, 20, 4403. Billah, U.H.; La, H.M.; Tavakkoli, A. Deep Learning-Based Feature Silencing for Accurate Concrete Crack Detection. Sensors 2020, 20, 4403.

Journal reference: Sensors 2020, 20, 4403
DOI: 10.3390/s20164403

Abstract

An autonomous concrete crack inspection system is necessary for preventing hazardous incidents arising from deteriorated concrete surfaces. In this paper, we represent 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 from concrete image. The proposed network alleviates the effect of gradient vanishing problem present in deep neural network architectures. A feature silencing module is incorporated in the crack detection framework, for eliminating unnecessary feature maps from the network. The overall performance of the network significantly improves as a result. 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 crack detection architectures present in literature.

Subject Areas

Convolutional Neural Network; Encoder-Decoder Architecture; Semantic Segmentation; Feature Silencing; Crack Detection

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