Compression and training method and apparatus for defect detection model

원문 (중국어)

Disclosed in the present application are a compression and training method and apparatus for a defect detection model. The method comprises: obtaining, by means of segmentation labeling, a segmentation labeling factor matrix of each sample image; inputting each sample image into both a first defect detection model and a second defect detection model, and extracting first feature maps outputted by target convolutional layers in the first defect detection model and second feature maps outputted by corresponding target convolutional layers in the second defect detection model; and calculating, by using the segmentation labeling factor matrix, corrected distances between corresponding feature vectors of the first feature maps and the second feature maps, and calculating, as a first loss function, the sum of the corrected distances between all the feature vectors of the first feature maps and the second feature maps. The present embodiment can improve the accuracy of detecting tiny product appearance defects by means of a compressed defect detection model.