Department of Engineering Mechanics, Hohai University, Nanjing 210098, China.
School of Aeronautics and Astronautics, Faculty of Vehicle Engineering and Mechanics, State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian 116024, China.
Sensors (Basel). 2020 Apr 3;20(7):2021. doi: 10.3390/s20072021.
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.
裂缝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）已经被证明是一种很有前途的工具，它可以通过自适应地从大量混凝土表面图像中识别裂缝特征，从而有效地识别裂缝的存在和演化。然而，传统 CNN 在裂缝识别中的准确性和多功能性在很大程度上受到限制，这是由于混凝土表面图像背景中包含的噪声的影响。噪声来源于多种来源，如光斑、模糊、表面粗糙度/磨损/污渍。为了提高基于 CNN 的裂缝识别方法的准确性、抗噪能力和多功能性，本研究建立了一个基于传统 CNN 与多层图像预处理策略（MLP）混合利用的增强型智能混凝土裂缝识别框架，其关键组件是同态滤波和 Otsu 阈值法。基于大量混凝土裂缝图像组成的数据集，通过比较和微调经典 CNN 结构，构建、训练和测试用于检测裂缝位置和识别裂缝类型的网络。通过对比研究，考察了包含 MLP 和 CNN 的框架在裂缝识别中的有效性和效率，以及是否实施了 MLP 策略。研究了不同来源和不同噪声水平影响下的裂缝识别准确性。
