IntroductionĬrack detection is one of the most important links of concrete structure maintenance, and it directly reflects how safe, durable, and applicable the concrete structure is. The results confirm that the proposed method can indeed detect cracks in images from real concrete surfaces. The trained CNN is integrated into a smartphone application to mobile more public to detect cracks in practice. The robustness and adaptability of the trained CNN are tested on 205 images with 3120 × 4160 pixel resolutions which were not used for training and validation. Through comparing validation accuracy under different base learning rates, 0.01 was chosen as the best base learning rate with the highest validation accuracy of 99.06%, and its training result is used in the following testing process. A CNN is designed through modifying AlexNet and then trained and validated using a built database with 60000 images. To overcome these challenges, this paper proposes an image-based crack detection method using a deep convolutional neural network (CNN). However, conventional image-based methods need extract crack features using complex image preprocessing techniques, so it can lead to challenges when concrete surface contains various types of noise due to extensively varying real-world situations such as thin cracks, rough surface, shadows, etc. Crack detection is important for the inspection and evaluation during the maintenance of concrete structures.
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