Patent ID: 11961278
Assignee: BEIJING XIAOMI PINECONE ELECTRONICS CO., LTD.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A method for detecting an occluded image, comprising:
after an image is captured by a camera, obtaining the image as an image to be detected;
inputting the image to be detected into a trained occluded-image detection model, wherein, the occluded-image detection model is trained based on original occluded images and non-occluded images by using a trained data feature augmentation network;
determining whether the image to be detected is an occluded image based on the occluded-image detection model; and
outputting an image detection result;
the method further comprising:
obtaining the original occluded images and the non-occluded images;
training the data feature augmentation network based on the original occluded images; and
training the occluded-image detection model based on the original occluded images and the non-occluded images by using the trained data feature augmentation network;
wherein training the data feature augmentation network based on the original occluded images comprises:
generating a finger template image and a non-finger image based on each original occluded image, wherein the finger template image is obtained by removing an area not occluded by a finger from the original occluded image, and the non-finger image is obtained by removing an area occluded by the finger from the original occluded image; and
generating a plurality of training data sets, wherein each training data set comprises one finger template image, one non-finger image and one original occluded image, and a plurality of training data sets are used to train the data feature augmentation network in each round of training;
wherein generating the finger template image and the non-finger image based on each original occluded image comprises:
obtaining mask data based on the original occluded image, the mask data indicating an occluded location in the original occluded image; and
generating the finger template image and the non-finger image based on the original occluded image and the mask data;
wherein training the occluded-image detection model based on the original occluded images and the non-occluded images by using the trained data feature augmentation network comprises:
obtaining a processed non-occluded image based on the mask data and the non-occluded image, wherein the processed non-occluded image is obtained by removing an area corresponding to the mask data from the non-occluded image;
inputting the finger template image and the processed non-occluded image into the trained data feature augmentation network to generate a generation feature of the non-occluded image;
inputting the original occluded image into a feature network to extract an original feature of the original occluded image, the occluded-image detection model comprising the feature network;
inputting the generation feature of the non-occluded image and the original feature of the original occluded image into a classification network for training, the occluded-image detection model comprising the classification network; and
determining that the training of the data feature augmentation network is finished when a loss function of the occluded-image detection model converges.