Food tray sealing fault detection using hyperspectral imaging and PCANet
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Food tray sealing fault detection using hyperspectral imaging and PCANet

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Food tray sealing fault detection using hyperspectral imaging and PCANet

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dc.contributor.author Benouis, Mohamed
dc.contributor.author Medus, Leandro Daniel
dc.contributor.author Saban, Mohamed
dc.contributor.author Labiak, Grzegorz
dc.contributor.author Rosado Muñoz, Alfredo
dc.date.accessioned 2021-04-16T14:37:00Z
dc.date.available 2021-04-16T14:37:00Z
dc.date.issued 2020
dc.identifier.uri https://hdl.handle.net/10550/78740
dc.description.abstract Food 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.
dc.language.iso eng
dc.relation.ispartof IFAC-PapersOnLine, 2020, vol. 53, num. 2, p. 7845-7850
dc.rights.uri info:eu-repo/semantics/openAccess
dc.source Benouis, Mohamed Medus, Leandro Daniel Saban, Mohamed Labiak, Grzegorz Rosado Muñoz, Alfredo 2020 Food tray sealing fault detection using hyperspectral imaging and PCANet IFAC-PapersOnLine 53 2 7845 7850
dc.subject Indústria agroalimentària
dc.subject Aliments Conservació
dc.subject Control de qualitat
dc.title Food tray sealing fault detection using hyperspectral imaging and PCANet
dc.type info:eu-repo/semantics/article
dc.date.updated 2021-04-16T14:37:00Z
dc.identifier.doi https://doi.org/10.1016/j.ifacol.2020.12.1955
dc.identifier.idgrec 145169

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