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From the quantitative and qualitative analysis of the experimental results of the crack detection experiments and the explanation of the results, as well as the evaluation analysis, it can be concluded that the CNN-based crack detection method proposed in this study has a good identification accuracy, especially in distinguishing between shallow cracks and complex cracks. It should be emphasized that the method can be used as a complementary method to the existing crack-type detection method, because it does not only detect the presence of cracks but also quantify them, and its accuracy is not affected by complex background interference or noise. The presence of cracks is the basic condition for pavement maintenance, and the quantitative detection of cracks is the basis for pavement management and crack management, so the proposed method is of great significance to provide a theoretical basis for crack detection and management in pavement maintenance and management.
The purpose of this study was to propose a CNN-based crack detection method. To achieve this, a deep convolutional neural network was proposed using a VGG-16 model with a fully connected layer added before the softmax layer. This experiment was based on real pavement images collected by a mobile camera, and the results were evaluated using the quadrupole crack classification and crack detection. The following conclusions can be drawn from the experimental results:
Crack detection accuracy is not affected by complex background interference and noise.
The number of crack pixels is not only less than that of the background pixels but is also less than that of the threshold, which is the foundation of the detection method.
The proposed method can achieve crack detection and classification with a high identification rate, especially in distinguishing between shallow cracks and complex cracks. It is also used to quantify cracks in the same way as existing methods.
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