Intelligent Identification Method and System for Aging Degree of Porcelain Glaze

An intelligent identification method for an aging degree of a porcelain glaze, comprising:

• acquiring, by a multispectral imaging device, reflection characteristics of a target porcelain in a visible light band and a near-infrared band, performing glaze reflection suppression processing on an acquired original image, and generating a standardized to-be-identified porcelain image;
• wherein the to-be-identified porcelain image is a porcelain image of the target porcelain in a preset image type, and the preset image type comprises a full-view image or a close-up image;
• loading the to-be-identified porcelain image into a pre-trained target glaze aging degree identification model, and acquiring a target glaze aging degree value of an aging degree classification result of each glaze region in a porcelain image output by the target glaze aging degree identification model;
• determining a target aging degree classification result of the target porcelain according to the target glaze aging degree value of the aging degree classification result of each glaze region in the porcelain image;
• and
• performing spatiotemporal alignment and fusion according to the target aging degree classification result and trace element data detected by X-ray fluorescence (XRF) spectroscopy, and generating a porcelain glaze aging degree identification report comprising an age dating result and glaze preservation state evaluation;
• wherein the target glaze aging degree identification model is acquired through training as follows:
• acquiring an initial glaze aging degree identification model, wherein the initial glaze aging degree identification model comprises a first glaze recognition model and at least one second glaze recognition model;
• each first image instance of the first glaze recognition model comprises a porcelain image of the target porcelain in the full-view image;
• and each second image instance of the second glaze recognition model comprises a porcelain image of the target porcelain in the close-up image;
• acquiring a first aging feature mapping parameter of the first glaze recognition model and a second aging feature mapping parameter of each second glaze recognition model upon completion of a current training stage;
• for each second glaze recognition model, determining a first directional parameter set of the first aging feature mapping parameter for fusion and a second directional parameter set of the second aging feature mapping parameter for fusion according to a direction consistency between a first parameter update direction and a second parameter update direction upon completion of each training stage of a latest target training stage;
• wherein the first parameter update direction is a model parameter update direction of the first aging feature mapping parameter of a first feature mapping component, and the second parameter update direction is a model parameter update direction of the second aging feature mapping parameter of a second feature mapping component;
• for each second glaze recognition model, fusing the first directional parameter set upon completion of the current training stage with the second directional parameter set of the second glaze recognition model, and acquiring the second aging feature mapping parameter corresponding to a next training stage;
• and performing one training iteration on the second glaze recognition model based on the acquired second aging feature mapping parameter and each second image instance of the second glaze recognition model, and acquiring the second aging feature mapping parameter upon completion of the next training stage;
• fusing the second directional parameter set of each second glaze recognition model upon completion of the next training stage with the first directional parameter set of the first glaze recognition model upon completion of the current training stage, and acquiring the first aging feature mapping parameter corresponding to the next training stage;
• and performing one training iteration on the first glaze recognition model based on the acquired first aging feature mapping parameter and each first image instance, and acquiring the first aging feature mapping parameter upon completion of the next training stage;
• wherein the first glaze recognition model upon completion of a last training stage is determined as a first glaze aging degree identification model, and the second glaze recognition model upon completion of the last training stage is determined as a second glaze aging degree identification model;
• and
• determining a glaze aging degree identification model comprising a feature extraction component corresponding to each image type of the preset image type from the first glaze aging degree identification model or the second glaze recognition model upon completion of training as the target glaze aging degree identification model.