Crack identification plays an essential role in the health diagnosis of various concrete structures. Among different intelligent algorithms, the convolutional neural networks (CNNs) has been demonstrated as a promising tool capable of efficiently identifying the existence and evolution of concrete cracks by adaptively recognizing crack features from a large amount of concrete surface images. However, the accuracy as well as the versatility of conventional CNNs in crack identification is largely limited, due to the influence of noise contained in the background of the concrete surface images. The noise originates from highly diverse sources, such as light spots, blurs, surface roughness/wear/stains. With the aim of enhancing the accuracy, noise immunity, and versatility of CNN-based crack identification methods, a framework of enhanced intelligent identification of concrete cracks is established in this study, based on a hybrid utilization of conventional CNNs with a multi-layered image preprocessing strategy (MLP), of which the key components are homomorphic filtering and the Otsu thresholding method. Relying on the comparison and fine-tuning of classic CNN structures, networks for detection of crack position and identification of crack type are built, trained, and tested, based on a dataset composed of a large number of concrete crack images. The effectiveness and efficiency of the proposed framework involving the MLP and the CNN in crack identification are examined by comparative studies, with and without the implementation of the MLP strategy. Crack identification accuracy subject to different sources and levels of noise influence is investigated.

中文翻译：

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使用多层图像预处理辅助的卷积神经网络增强对混凝土裂缝的智能识别。

裂纹识别在各种混凝土结构的健康诊断中起着至关重要的作用。在不同的智能算法中，卷积神经网络（CNN）已被证明是一种有前途的工具，能够通过自适应地从大量混凝土表面图像中识别裂缝特征来有效地识别混凝土裂缝的存在和发展。然而，由于混凝土表面图像的背景中所包含的噪声的影响，常规CNN在裂缝识别中的准确性以及通用性受到很大限制。噪声源自高度多样化的来源，例如光斑，模糊，表面粗糙度/磨损/污点。为了提高基于CNN的裂纹识别方法的准确性，抗噪性和多功能性，基于常规CNN与多层图像预处理策略（MLP）的混合利用，本研究建立了增强的混凝土裂缝智能识别框架，其关键成分是同态滤波和Otsu阈值化方法。依靠经典CNN结构的比较和微调，基于由大量混凝土裂纹图像组成的数据集，构建，训练和测试了用于检测裂纹位置和识别裂纹类型的网络。通过比较研究，在有或没有实施MLP策略的情况下，通过比较研究来检验涉及MLP和CNN的提议框架的有效性和效率。研究了受不同来源和噪声影响水平的裂纹识别精度。