Abnormal segmentation method of self-supervised learning negative bidirectional distillation

Abstract

The invention discloses a self-supervised learning negative two-way distillation anomaly segmentation method, which comprises the steps of preprocessing a non-anomaly unsupervised anomaly detection training data set, and constructing a simulated anomaly data set; training a student network encoder by combining knowledge distillation and negative learning thoughts according to the simulated abnormal data set; performing multi-scale feature fusion on an output result of the encoder; inputting a feature fusion result into a student network decoder, and training the decoder through a reverse distillation and self-supervision anomaly reconstruction task; performing feature fusion on output results of corresponding layers of the student network and the teacher network by using feature fusion based on a channel attention mechanism; and taking the fused features as input to train a segmentation network so as to accurately predict an abnormal position. According to the method, a bidirectional distillation network structure is constructed, and a self-supervised reconstruction task is combined with a negative learning idea, so that the decision-making ability of the model is effectively enhanced, and the image segmentation accuracy in anomaly detection is remarkably improved.

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