During the operating lifecycle of civil structures, cracks will occur inevitably, which may pose great threat to the safety of the structures without timely maintenance. Digital image processing techniques have great potential in automatically detecting cracks, which can replace the labor-intensive and highly subjective traditional manual on-site inspections. Therefore, this paper presents a crack detection technology based on a convolutional neural network, GoogLeNet Inception V3. Firstly, a crack image dataset is acquired and constructed, which includes 2682 images with cracks and 983 images without crack at a resolution of 256 Ã— 256 pixels. Then, based on a transfer learning method, the pretrained GoogLeNet Inception V3 model is retrained by the crack dataset for better identifying the crack images. The accuracy of the final trained model on the test set can reach 0.985. Moreover, image stitching based on Oriented FAST and Rotated BRIEF feature matching algorithm is realized, in order to overcome the limitation of camera field of view. Compared with the traditional image processing technology, the method adopted in this work can automatically study the characteristics of the object from the dataset, which can adapt to the complex real environment. Due to the transfer learning method, the crack detection can be achieved based on the existing well-trained models after being retrained by a small dataset.

中文翻译：

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基于图像拼接和预训练卷积神经网络迁移学习的表面裂纹检测

在土木结构的运行生命周期中，不可避免地会出现裂缝，如果不及时维护，可能对结构的安全构成极大的威胁。数字图像处理技术在自动检测裂纹方面具有巨大潜力，可以替代劳动强度大、主观性强的传统人工现场检测。因此，本文提出了一种基于卷积神经网络的裂纹检测技术，GoogLeNet Inception V3。首先，获取并构建裂纹图像数据集，包括2682幅裂纹图像和983幅无裂纹图像，分辨率为256×256像素。然后，基于迁移学习方法，通过裂纹数据集对预训练的 GoogLeNet Inception V3 模型进行再训练，以更好地识别裂纹图像。最终训练好的模型在测试集上的准确率可以达到 0.985。此外，实现了基于Oriented FAST和Rotated Brief特征匹配算法的图像拼接，以克服相机视场的限制。与传统的图像处理技术相比，本文采用的方法可以从数据集中自动研究对象的特征，能够适应复杂的真实环境。由于采用迁移学习方法，可以在现有训练好的模型的基础上通过小数据集重新训练后实现裂纹检测。与传统的图像处理技术相比，本文采用的方法可以从数据集中自动研究对象的特征，能够适应复杂的真实环境。由于采用迁移学习方法，可以在现有训练好的模型的基础上通过小数据集重新训练后实现裂纹检测。与传统的图像处理技术相比，本文采用的方法可以从数据集中自动研究对象的特征，能够适应复杂的真实环境。由于采用迁移学习方法，可以在现有训练好的模型的基础上通过小数据集重新训练后实现裂纹检测。