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The UAV images shown in Figure 2 were taken by two UAVs, and the original images are shown in Figure 8. In the complete large-scale UAV images, the vegetation and other types of surface objects are obvious. In the sub-images, the number of green pixels and the number of other types of surface objects are greatly reduced. In addition, the dimensions of the original image are reduced, and the image resolution is increased, thereby allowing a clear distinction between the cracks and other types of surface objects. Therefore, the UAV image cracks are clearly distinguished from other types of surface objects, and the crack extraction effect is improved. The UAV image cutting method is shown in Figure 9. In this paper, the UAV image was cut into sub-images with a size of 50 × 50, and the width and height of the image were twice the height and length of the mountain, respectively. First, the UAV image was transferred from the original image space to the image coordinates, and the image was rotated by 90° to make the image surface parallel to the image coordinate axis. Then, the image was cut according to the pixels of the mountain, with the pixels at the middle of the mountain as the cutting point. The UAV image cutting results are shown in Figure 10.
The UAV image is densely distributed with green pixels (the green color of the vegetation is generally larger than that of other types of surface objects), and the background information of the image is rich. Therefore, the UAV image is relatively easy to distinguish between cracks and other types of surface objects. The SVM machine learning method is based on the statistical distribution characteristics of crack and non-crack pixels in the images and the principal component analysis (PCA) method is used to extract and reduce the dimensions of the crack-containing sub-images.
Laplace sharpening is a high-pass filtering method [23], and the main purpose is to remove the low-frequency noises and enhance the crack edges. A mathematical model of Laplace sharpening is given as follows:
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