Road disease picture enhancement method coupling traditional method and deep convolutional generative adversarial network
A deep convolution, road disease technology, applied in the field of image processing, can solve problems such as unconstrained and uncontrollable, and achieve the effect of strong generalization ability, good model effect, and reduction of labor cost and time loss.
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[0040] The original pavement lesion image data set used in the present invention is a three-channel grayscale image. The specific implementation steps are as follows:
[0041] (1) Manual marking
[0042] The purpose of human labeling is to classify pavement disease datasets for supervised learning. In supervised learning, the size of the data set and the consistency of classification features will have a great impact on the prediction accuracy of the network. Therefore, in this step, the present invention uses manual calibration method to classify and screen, and labelImg is used to calibrate the pavement disease picture collection, and obtains transverse cracks, longitudinal cracks, reticular cracks, potholes, pavement markings and pavement damage markings. Typical pictures of six categories, such as Figure 4 .
[0043] (2) Batch cutting
[0044] In order not to destroy the classification features such as pavement cracks, but also to reduce the difficulty of training ca...
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