Crack P Code Matlab For Neural Network BETTER
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figure 10 illustrates the workflow of crack detection. first, the raw images taken from concrete surface are converted to grayscale images and then resized to 96×256 pixels resolution. the image is then vertically flipped and horizontally flipped to generate 28945068 raw training images. in this paper, the raw images are cropped into smaller images with the size of 96×256 pixels to obtain 2898237 training images. 60000 images are chosen from the database as the raw images for validation, and another set of 60000 images is picked out as the raw images for testing. the cropped images are transformed to rgb color representation and input to the cnn to obtain the feature of each convolution kernels, in which the trained cnn classifier is the last layer to classify the image into images with and without cracks. finally, the trained cnn classifier in section 4.3 is used to detect cracks in the raw images and their results are overlapped with the exhaustive search method for crack detection in section 4.1. the overlapped results are ground truth.
the proposed method was validated on 2 concrete surfaces, and the results demonstrated that the crack numbers detected by the cnn classifier were found in the ground truth. the detected crack numbers also agreed with the ground truth in one-to-one relations, and the averaged crack precision and recall were 100.00% and 100.00% on these two concrete surfaces. figure 11 shows that the detected crack numbers of the proposed method were consistent with the ground truth. figure 11(a) shows the detected crack numbers of the first concrete surface, in which several tiny cracks which are hard to detect with the exhaustive search method were detected by the cnn classifier. figure 11(b) shows the detected crack numbers of the second concrete surface, in which cracks were also detected and measured properly.
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