Yarn-out state detection method and apparatus, device and storage medium

A method for training a yarn-out state detection model, comprising:

• performing morphological processing on a sample image, to obtain a third image;
• dividing the third image into a plurality of segments in a length direction of a target yarn path, to obtain a plurality of sample sub-images;
• performing feature extraction on the plurality of sample sub-images, to obtain a plurality of sample sub-image features;
• obtaining a training image feature of the target yarn path, according to the plurality of sample sub-image features and corresponding weight values, wherein the weight values are determined according to width values of yarn paths in the plurality of sample sub-images;
• performing short-time Fourier transform on laser sample data, to obtain a time-frequency representation result;
• determining a time-frequency feature, according to the time-frequency representation result;
• obtaining a training laser feature of the target yarn path, according to the time-frequency feature;
• using a first detection model, to obtain a predicted result of a state category of the target yarn path according to the training image feature and the training laser feature;
• determining a loss function according to the predicted result and an actual result of the state category of the target yarn path;
• and
• updating a parameter of the first detection model based on the loss function to obtain a trained yarn-out state detection model.