LAUSR

Chinese liquor (Baijiu) is one of the four major distilled spirits in the world. At present, liquor products containing impurities still exist on the market, which not only damage corporate image but also endanger consumer health. Due to the production process and packaging technologies, impurities usually appear in products of Baijiu before entering the market, such as glass debris, mosquitoes, aluminium scraps, hair, and fibres. In this paper, a novel method for detecting impurities in bottled Baijiu is proposed. Firstly, the region of interest (ROI) is cropped by analysing the histogram projection of the original image to eliminate redundant information. Secondly, to adjust the number of distributions in the Gaussian mixture model (GMM) dynamically, multiple unmatched distributions are removed and distributions with similar means are merged in the process of modelling the GMM background. Then, to adaptively change the learning rates of the front and background pixels, the learning rate of the pixel model is created by combining the frame difference results of the sequence images. Finally, a histogram of oriented gradient (HOG) features of the moving targets is extracted, and the Support Vector Machine (SVM) model is chosen to exclude bubble interference. The experimental results show that this impurity detection method for bottled Baijiu controls the missed rate by within 1% and the false detection rate by around 3% of impurities. Its speed is five times faster than manual inspection and its repeatability index is good, indicating that the overall performance of the proposed method is better than manual inspection with a lamp. This method is not only efficient and fast, but also provides practical, theoretical, and technical support for impurity detection of bottled Baijiu that has broad application prospects.