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This invention discloses a few-sample inductor core surface defect detection method based on model interaction, belonging to the fields of machine vision and industrial defect detection technology. The method includes the following steps: S1 Data acquisition and image preprocessing, constructing normal samples, labeled samples, and unlabeled samples; S2 Constructing an unsupervised statistical model based on statistical learning; S3 Constructing a supervised semantic segmentation model; S4 Simultaneously inputting the inductor core image to be detected into both the unsupervised statistical model and the supervised semantic segmentation model for processing, generating segmentation results; S5 Quantifying the differences in detection results; S6 Updating the parameters of the unsupervised statistical model; S7 Generating pseudo-labels based on the unsupervised statistical model; S8 Updating the weights of the supervised semantic segmentation model; S9 Using batches of processed images to be detected, inputting them into the updated unsupervised statistical model and the supervised semantic segmentation model for detection, obtaining detection results, and analyzing and calculating system performance indicators.