Abnormal image detection method combining attention mechanism and information entropy minimization

Abstract

The invention relates to an attention mechanism and information entropy minimization combined abnormal image detection method, which comprises the following steps of: (1) selecting and integrating a current mainstream data set as a training data set; (2) selecting a deep neural network for novelty detection, and improving the structure of a mainstream detection model; (3) preprocessing the sampleimage to be suitable for network input; (4) training a detection model; and (5) detecting by using the trained detection model to obtain a detection result. The invention provides an abnormal image detection method combining an attention mechanism and information entropy minimization. By making a certain improvement on a mainstream method, the model has better representation on normal sample characteristics after being trained, the sensitivity to abnormal samples is relatively high, and the discrimination capability of novelty detection on normal samples and abnormal samples is improved.

Description

technical field [0001] The present invention proposes an abnormal image detection method combining attention mechanism and information entropy minimization, which has a stronger ability to identify new or unknown samples. Background technique [0002] Abnormal image detection has a wide range of applications. The abnormal image detection task requires the model to have the ability to identify new or unknown samples. The key to abnormal image detection is to make the model have good expressive ability for normal samples and maintain a certain sensitivity for abnormal samples. In general, both capabilities of the model should be enhanced during training. A common assumption in abnormal image detection is that abnormal samples not only have characteristic differences from normal samples in high-dimensional data space, but also in low-dimensional data space. Therefore, the key to the abnormal image detection task becomes how to better reconstruct the high-dimensional data spa...

AI Technical Summary

Problems solved by technology

[0006] However, these deep neural network models still face a problem: the models trained only by normal samples not only have good generalization ability to normal samples, but also have a certain generalization ability to abnormal samples, which leads to the inference of the applied model. Abnormal samples do not have good discrimination ability

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