Method for detecting cigarette appearance defect based on variational bayesian inference

Abstract

The present application discloses a method for detecting cigarette appearance defects based on variational Bayesian inference. In a cigarette appearance defect detection scenario, in order to solve the problem that a current point estimation-based machine learning algorithm leads to an overconfidence decision in a data scarcity area, the present application proposes a variational inference-based Bayesian method to improve a SSD backbone network, preferably including calculating a real posterior probability by the variational inference method, replacing a point estimation mechanism of a convolution layer weight in the original backbone network by using a form of probability distribution, then detecting the improved extracted features by an SSD target detection algorithm, and furthermore, accurately determining whether the appearance of the cigarette is defective and the type and location of the defect. The present application can improve the accuracy of defect detection without losing the processing speed, not only saving a large amount of manpower and material resources, but also effectively providing reliable technical support for finding the root causes of defect types. Thus, it achieves intelligent quality risk management and control in a cigarette production workshop.

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