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Nevertheless, the former approaches [8, 3, 5] rely on image binarization, but they lack in terms of crack quality and accuracy. The latter approach [5] is incapable of assessing the crack defect density. In addition, the experiments in that study were only conducted on a postdisaster building. To the best of our knowledge, no study is available for the development of a crack detection system utilizing machine learning-based image processing.

In this study, we developed a novel crack detection system using an image processing approach based on deep learning. The proposed system solves the aforementioned challenges and enhances the detection and measurement of cracks. The crack detection is mainly based on two steps: feature extraction and crack identification. In the feature extraction step, we propose a three-layer convolutional neural network (CNN) to deal with unprocessed images. The CNN is able to extract useful features for crack identification from raw images. The identification step is based on a convolutional neural network (CNN). The proposed system analyzes and detects cracks in both building and nonbuilding materials. Moreover, this system does not require preprocessing of images. The performance of the proposed system is demonstrated in four experiments.

In this study, we propose a novel crack detection system based on an image processing approach using deep learning. Specifically, we aim to provide a sufficient crack detection for both building and nonbuilding materials. The proposed system is capable of addressing the challenges in crack detection, namely, low contrast, low signal-to-noise ratio, uneven illumination, severe noise pollution, and existence of blemishes, spall, or surface cracks. The proposed system has the following characteristics:

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