Method and system for fine identification of types of ancient coins based on artificial intelligence (ai) architecture optimization

A method for fine identification of types of ancient coins based on artificial intelligence (AI) architecture optimization, comprising:

• acquiring, by a multispectral imaging device, original ancient coin image data, performing illumination equalization processing and noise filtering on the original ancient coin image data, and generating a standardized initial ancient coin image;
• performing enhancement processing on the initial ancient coin image, and generating a target ancient coin image, wherein the enhancement processing comprises texture enhancement processing based on an edge-preserving algorithm;
• invoking a pre-trained target ancient coin identification model to perform ancient coin type identification on the target ancient coin image, and obtaining an ancient coin type identifier of the target ancient coin image;
• and
• triggering directional retrieval from a preset historical ancient coin database based on the ancient coin type identifier, outputting ancient coin-related data corresponding to the ancient coin type identifier, and generating a fine identification report of an ancient coin type based on the ancient coin-related data;
• wherein the target ancient coin identification model is obtained through training by following steps:
• acquiring an ancient coin identification model and a sample ancient coin image, wherein ancient coin training images of the sample ancient coin image comprise a first ancient coin training image and second ancient coin training images;
• and the first ancient coin training image is labeled with a corresponding ancient coin type identifier, and the second ancient coin training images are unlabeled ancient coin training images;
• for each of the ancient coin training images, performing region division processing on the ancient coin training image, and acquiring a plurality of ancient coin sub-images of the ancient coin training image;
• performing, by the ancient coin identification model, feature extraction processing on the ancient coin training image and each of the ancient coin sub-images of the ancient coin training image, and obtaining a coarse-grained image feature of the ancient coin training image and a fine-grained image feature of each of the ancient coin sub-images;
• performing contrastive learning on the coarse-grained image feature of each of the ancient coin training images of the sample ancient coin image according to the ancient coin type identifier of the first ancient coin training image, and obtaining an ancient coin type identifier of the second ancient coin training images;
• constructing a coarse-grained feature set according to the coarse-grained image feature of each of the ancient coin training images, and constructing a fine-grained feature set according to the fine-grained image feature of the ancient coin sub-image of each of the ancient coin training images;
• for each of the ancient coin training images, traversing at least one neighboring coarse-grained image feature neighboring the coarse-grained image feature of the ancient coin training image in the coarse-grained feature set, and determining a category candidate of the ancient coin training image according to an ancient coin type identifier of the ancient coin training image to which the neighboring coarse-grained image feature belongs;
• for each of the ancient coin sub-images of the ancient coin training image, traversing at least one neighboring fine-grained image feature neighboring the fine-grained image feature of the ancient coin sub-image in the fine-grained feature set, and determining a category candidate of the ancient coin sub-image according to the ancient coin type identifier of the ancient coin training image to which the neighboring fine-grained image feature belongs;
• performing probability distribution modeling on the ancient coin type identifier of the category candidate of the ancient coin sub-image, and determining an ancient coin type confidence distribution of the ancient coin sub-image;
• performing probability distribution modeling on the ancient coin type identifier of the category candidate of the ancient coin training image to which the ancient coin sub-image belongs, and determining an ancient coin type confidence distribution of the ancient coin training image to which the ancient coin sub-image belongs;
• calculating a multi-granularity feature alignment metric between the ancient coin sub-image and the ancient coin training image to which the ancient coin sub-image belongs according to the ancient coin type confidence distribution of the ancient coin sub-image and the ancient coin type confidence distribution of the ancient coin training image to which the ancient coin sub-image belongs;
• performing ancient coin type identification on the ancient coin training image according to the coarse-grained image feature of the ancient coin training image, and obtaining a coarse-grained type confidence distribution corresponding to the ancient coin training image;
• performing ancient coin type identification on the ancient coin sub-image according to the fine-grained image feature of the ancient coin sub-image, and obtaining a fine-grained type confidence distribution corresponding to the ancient coin sub-image;
• and
• performing iterative optimization on a parameter of the ancient coin identification model according to the coarse-grained type confidence distribution, the fine-grained type confidence distribution, the multi-granularity feature alignment metric and the ancient coin type identifier of the second ancient coin training images, and obtaining the target ancient coin identification model.