Shi Xiaoshi, Tang Zuoliang, Wang Yihan, Xie Hong, Xu Lijia
College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya'an 625014, China.
College of Resources, Sichuan Agricultural University, Chengdu 611130, China.
Foods. 2022 May 17;11(10):1444. doi: 10.3390/foods11101444.
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.
白酒是世界四大蒸馏酒之一。目前，市场上仍存在含有杂质的酒类产品，这不仅损害企业形象，还危及消费者健康。由于生产工艺和包装技术的原因，杂质通常在白酒产品进入市场前就已出现，如玻璃碎片、蚊虫、铝屑、毛发和纤维等。本文提出了一种检测瓶装白酒中杂质的新方法。首先，通过分析原始图像的直方图投影来裁剪感兴趣区域（ROI），以消除冗余信息。其次，为了动态调整高斯混合模型（GMM）中的分布数量，在对GMM背景建模的过程中，去除多个不匹配的分布并合并均值相似的分布。然后，为了自适应地改变前景和背景像素的学习率，通过结合序列图像的帧差结果来创建像素模型的学习率。最后，提取运动目标的方向梯度直方图（HOG）特征，并选择支持向量机（SVM）模型来排除气泡干扰。实验结果表明，这种瓶装白酒杂质检测方法将杂质的漏检率控制在1%以内，误检率控制在3%左右。其速度比人工检测快五倍，重复性指标良好，表明该方法的整体性能优于人工灯下检测。该方法不仅高效快速，还为瓶装白酒的杂质检测提供了实用的理论和技术支持，具有广阔的应用前景。
