Abstract: In order to detect cigarette carton missing in cigarette case, an intelligent detection method based on similarity analysis and threshold self-correction was proposed on the basis of the front position and rear position images inside cigarette case. Firstly, the front position and rear position images were processed by gradation transformation and image segmentation; based on the horizontal and vertical segmentation of the processed images, 16 feature similarity indicators of the processed images were calculated by Pearson similarity analysis; and the thresholds of the 16 indicators for all modelling images were determined by kernel density estimation. Secondly, the 16 feature similarity indicators of the front position and rear position images were calculated by horizontal and vertical block similarity analysis method, and carton missing in the current front and rear images was judged via comparing with the thresholds. Finally, the feature similarity indicators of the new without carton missing images were added to the feature similarity matrix of the modelling images, and threshold self-correction was carried out with kernel density estimation. Off-line validation was carried out on the basis of actual running data of cigarette case filling process in Hangzhou Cigarette Factory, the results showed that the proposed method realized the effective characterization and quantification of feature information of the images inside cigarette cases and could accurately detect the carton missing in cigarette cases. The detection accuracy reached 100% for all 15 types of carton missing in the test data, and the labeled images provided the basis for carton missing tracing. The proposed method provides a technical support for improving the adaptability and reliability of detection method for carton missing in cigarette case.