食品与机械2023，Vol.39Issue(12)：42-47,69,7.DOI:10.13652/j.spjx.1003.5788.2023.60149
计算机视觉结合深度学习技术快速鉴别八角粉掺伪
The application of computer vision combining with deep leaning techniques for rapid discrimination of adulterated star anise powder
陈劲星1
作者信息
- 1. 福建中检华日食品安全检测有限公司,福建福州 350008
- 折叠
摘要
Abstract
Objective:This study aims to design a novel approach,utilizing computer vision combining with deep learning,for rapid determination the adulteration in star anise powder.Methods:Collected the original images of star anise powder with varying adulteration ratios.Employing preprocessing and data enhancement techniques,an image dataset was curated.Subsequently,a SqueezeNet model was constructed and compared with five machine learning models,including Support Vector Machine(SVM),K-Nearest Neighbor Learning(KNN),Random Forest(RF),Gradient Boosting Tree(GBT),and Multilayer Perceptron(MLP).Results:The highest accuracy achieved by the five machine learning models was only 66.37％,while the accuracy of the SqueezeNet model was 99.42％.The results showed that SqueezeNet model was better than these machine learning models in identifying the adulteration in star anise powder.Conclusion:The proposed detection method based on computer vision combining with SqueezeNet model can effectively identify the adulteration in star anise powder.This method is easy to operate,and provides a novel technique for the rapid detection of food adulteration.关键词
八角/掺伪鉴别/深度学习/视觉技术/SqueezeNet模型Key words
star anise powder/adulteration identification/deep learning/visual technology/SqueezeNet model引用本文复制引用
陈劲星..计算机视觉结合深度学习技术快速鉴别八角粉掺伪[J].食品与机械,2023,39(12):42-47,69,7.
