Terracotta army fragment classification method based on deep learning
A technology of deep learning and classification methods, applied in neural learning methods, instruments, biological neural network models, etc., can solve the problems of inaccurate classification, low measurement accuracy, and high degree of experience dependence, and achieve the effect of convenient operation
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[0063] Such as figure 1 As shown, the fragment data set of the Terracotta Warriors in this embodiment is the fragment model collected by the researchers of the National and Local Joint Engineering Research Center of Cultural Heritage Digitalization of Northwest University to the Museum of Terracotta Warriors and Horses. There are more than 500 existing fragment models, and a large Some have been accurately labeled. Divide the fragments into 6 body parts, including arms, feet, skirts, upper body, hands and legs, as 6 categories, and capture the images of the outer surface of the fragments. Each fragment model has about 10 screenshots, a total of 2000 Multiple pictures.
[0064] The following are the specific steps:
[0065] Step 1: Use python to preprocess the image of the terracotta warriors, remove the background of the picture, cut off the redundant white edges, and then unify the RGB image with a picture size of 227*227, and perform normalization processing to obtain 1800...
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