Deep Learning-Based Feature Silencing for Accurate Concrete Crack Detection
Deep Learning-Based Feature Silencing for Accurate Concrete Crack Detection
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DOI：
10.3390/s20164403
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发表时间：
2020-08-01
期刊：
影响因子：
3.9
通讯作者：
Tavakkoli, Alireza
中科院分区：
文献类型：
--
作者：
Billah, Umme Hafsa;La, Hung Manh;Tavakkoli, Alireza
混凝土裂缝自动检测系统是防止混凝土表面劣化事故发生的必要手段。在本文中，我们提出了一个混凝土裂缝检测框架，以帮助自动化检测过程。该方法采用深度卷积神经网络结构进行裂缝分割，同时解决了梯度消失问题的影响。在该框架中加入了特征沉默模块，能够消除网络中的非判别特征映射以提高性能。实验结果支持在卷积神经网络架构中加入特征沉默的好处，以提高网络的鲁棒性、灵敏度和特异性。所提出的体系结构的另一个好处是它能够适应相对于目标应用程序的特异性（正类检测精度）和灵敏度（负类检测精度）之间的权衡。此外，所提出的框架比最先进的裂纹检测体系结构实现了更高的精度率和处理时间。
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.