文章基本信息

标题：Food tray sealing fault detection using hyperspectral imaging and PCANet
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作者：Mohamed Benouis ; Leandro D. Medus ; Mohamed Saban 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2020
卷号：53
期号：2
页码：7845-7850
DOI：10.1016/j.ifacol.2020.12.1955
语种：English
出版社：Elsevier
摘要：AbstractFood trays are very common in shops and supermarkets. Fresh food packaged in trays must be correctly sealed to protect the internal atmosphere and avoid contamination or deterioration. Due to the speed of production, it is not possible to have human quality inspection. Thus, automatic fault detection is a must to reach high production volume. This work describes a deep neural network based on Principal Component Analysis Network (PCANet) for food tray sealing fault detection. The input data come from hyperspectral cameras, showing more characteristics than regular industrial cameras or the human eye as they capture the spectral properties for each pixel. The proposed classification algorithm is divided into three main parts. In the first part, a single image is extracted from the hypercube by using pixel-level fusion method: the cube hyperspectral images are transformed into two-dimensional images to use as the input to the PCANet. Second, a PCANet structure is applied to the fused image. The PCANet has two filter bank layers and one binarization layer (three stages), obtaining a feature vector. Finally, a classification algorithm is used, having the feature vector as input data. The SVM and KNN classifiers were used. The database used in this work is provided by food industry professionals, containing eleven types of contamination in the seal area of the food tray and using metallic opaque cover film. Obtained results show that the design of our framework proposed achieves accuracy of 90% (87% F-measure) and 89% (89% F-measure) for SVM and KNN, respectively. Computation time for classification shows that a food tray speed of 65 trays per second could be reached. As a final result, the influence of the dataset size is analyzed, having PCANet a similar behavior for an extended and a reduced dataset.
关键词：KeywordsImage processingPCANet ClassifierNeural networksFood IndustryFood packagingquality inspection