Department of Electrical and Electronic Engineering, Ewha Womans University, Seoul 03760, Korea.
Department of Architectural and Urban Systems Engineering, Ewha Womans University, Seoul 03760, Korea.
Sensors (Basel). 2019 Nov 4;19(21):4796. doi: 10.3390/s19214796.
Image sensors are widely used for detecting cracks on concrete surfaces to help proactive and timely management of concrete structures. However, it is a challenging task to reliably detect cracks on damaged surfaces in the real world due to noise and undesired artifacts. In this paper, we propose an autonomous crack detection algorithm based on convolutional neural network (CNN) to solve the problem. To this aim, the proposed algorithm uses a two-branched CNN architecture, consisting of sub-networks named a crack-component-aware (CCA) network and a crack-region-aware (CRA) network. The CCA network is to learn gradient component regarding cracks, and the CRA network is to learn a region-of-interest by distinguishing critical cracks and noise such as scratches. Specifically, the two sub-networks are built on convolution-deconvolution CNN architectures, but also they are comprised of different functional components to achieve their own goals efficiently. The two sub-networks are trained in an end-to-end to jointly optimize parameters and produce the final output of localizing important cracks. Various crack image samples and learning methods are used for efficiently training the proposed network. In the experimental results, the proposed algorithm provides better performance in the crack detection than the conventional algorithms.
图像传感器广泛应用于检测混凝土表面的裂缝，以帮助对混凝土结构进行主动和及时的管理。然而，由于噪声和不需要的伪影，在现实世界中可靠地检测受损表面上的裂缝是一项具有挑战性的任务。在本文中，我们提出了一种基于卷积神经网络（CNN）的自主裂缝检测算法来解决这个问题。为此，所提出的算法使用了一种由两个分支的 CNN 结构组成的算法，该结构由两个子网络组成，分别是裂缝成分感知（CCA）网络和裂缝区域感知（CRA）网络。CCA 网络用于学习与裂缝有关的梯度分量，而 CRA 网络则通过区分关键裂缝和划痕等噪声来学习感兴趣的区域。具体来说，这两个子网络建立在卷积反卷积 CNN 架构上，但它们也包含不同的功能组件，以有效地实现各自的目标。这两个子网络以端到端的方式进行训练，共同优化参数，并生成定位重要裂缝的最终输出。使用了各种裂缝图像样本和学习方法来有效地训练所提出的网络。在实验结果中，所提出的算法在裂缝检测方面的性能优于传统算法。
