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.