A method based on the baseline model of the visual characteristics of images (BMVCI) is proposed to detect cracks in concrete structures. BMVCI refers to the model, which consists of images of the noncrack areas of a concrete structure with cracks or images of the noncrack state of a concrete structure. Compared with the performance of edge detection (ED) methods for detecting cracks in concrete structures, this baseline model expands the quasi-distance between the edges of cracks and the image background; thus, the crack detection accuracy is effectively improved. Additionally, the discriminative threshold of cracks is quantitatively determined with BMVCI, which avoids the influence of artificial interference when determining the abovementioned threshold used for ED methods. Meanwhile, compared with the methods based on artificial intelligence, such as deep learning (DL), the calculating efficiency of the proposed method is higher because the proposed method converts the high-dimensional image data into low-dimensional digital features for training. With the same small size set of training samples, the accuracy of the crack detection of the proposed method is higher than that of the methods based on the framework of DL. In this study, Gaussian convolution is applied to generate the visual characteristics of images, and then a kernel principal component analysis-based method is implemented to establish the BMVCI. The basic idea of novelty detection is applied to detect cracks in concrete structures. Finally, an experiment on concrete structures is designed and applied to demonstrate the effectiveness of the proposed method.

中文翻译：

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使用图像视觉特征的基线模型检测混凝土结构中的裂缝

提出了一种基于图像视觉特征基线模型（BMVCI）的混凝土结构裂缝检测方法。BMVCI 是指模型，它由具有裂缝的混凝土结构的非裂缝区域的图像或混凝土结构的非裂缝状态的图像组成。与边缘检测（ED）方法检测混凝土结构裂缝的性能相比，该基线模型扩大了裂缝边缘与图像背景之间的准距离；从而有效地提高了裂纹检测精度。此外，裂纹的判别阈值采用 BMVCI 定量确定，避免了在确定上述用于 ED 方法的阈值时受到人为干扰的影响。同时，与基于人工智能的方法相比，例如深度学习（DL），该方法的计算效率更高，因为该方法将高维图像数据转换为低维数字特征进行训练。在训练样本量相同的情况下，所提方法的裂纹检测精度高于基于深度学习框架的方法。在这项研究中，应用高斯卷积来生成图像的视觉特征，然后实现基于核主成分分析的方法来建立 BMVCI。新奇检测的基本思想应用于检测混凝土结构中的裂缝。最后，设计并应用混凝土结构实验来证明所提方法的有效性。该方法的计算效率更高，因为该方法将高维图像数据转换为低维数字特征进行训练。在训练样本量相同的情况下，所提方法的裂纹检测精度高于基于深度学习框架的方法。在这项研究中，应用高斯卷积来生成图像的视觉特征，然后实现基于核主成分分析的方法来建立 BMVCI。新奇检测的基本思想应用于检测混凝土结构中的裂缝。最后，设计并应用混凝土结构实验来证明所提方法的有效性。该方法的计算效率更高，因为该方法将高维图像数据转换为低维数字特征进行训练。在训练样本量相同的情况下，所提方法的裂纹检测精度高于基于深度学习框架的方法。在这项研究中，应用高斯卷积来生成图像的视觉特征，然后实现基于核主成分分析的方法来建立 BMVCI。新奇检测的基本思想应用于检测混凝土结构中的裂缝。最后，设计并应用混凝土结构实验来证明所提方法的有效性。