Food Tray Sealing Fault Detection in Multi-Spectral Images Using Data Fusion and Deep Learning Techniques
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Food Tray Sealing Fault Detection in Multi-Spectral Images Using Data Fusion and Deep Learning Techniques

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Food Tray Sealing Fault Detection in Multi-Spectral Images Using Data Fusion and Deep Learning Techniques

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dc.contributor.author Benouis, Mohamed
dc.contributor.author Medus, Leandro Daniel
dc.contributor.author Saban, Mohamed
dc.contributor.author Ghemougui, Abdessattar
dc.contributor.author Rosado Muñoz, Alfredo
dc.date.accessioned 2022-04-11T18:36:01Z
dc.date.available 2022-04-11T18:36:01Z
dc.date.issued 2021
dc.identifier.uri https://hdl.handle.net/10550/82278
dc.description.abstract A correct food tray sealing is required to preserve food properties and safety for consumers. Traditional food packaging inspections are made by human operators to detect seal defects. Recent advances in the field of food inspection have been related to the use of hyperspectral imaging technology and automated vision-based inspection systems. A deep learning-based approach for food tray sealing fault detection using hyperspectral images is described. Several pixel-based image fusion methods are proposed to obtain 2D images from the 3D hyperspectral image datacube, which feeds the deep learning (DL) algorithms. Instead of considering all spectral bands in region of interest around a contaminated or faulty seal area, only relevant bands are selected using data fusion. These techniques greatly improve the computation time while maintaining a high classification ratio, showing that the fused image contains enough information for checking a food tray sealing state (faulty or normal), avoiding feeding a large image datacube to the DL algorithms. Additionally, the proposed DL algorithms do not require any prior handcraft approach, i.e., no manual tuning of the parameters in the algorithms are required since the training process adjusts the algorithm. The experimental results, validated using an industrial dataset for food trays, along with different deep learning methods, demonstrate the effectiveness of the proposed approach. In the studied dataset, an accuracy of 88.7%, 88.3%, 89.3%, and 90.1% was achieved for Deep Belief Network (DBN), Extreme Learning Machine (ELM), Stacked Auto Encoder (SAE), and Convolutional Neural Network (CNN), respectively.
dc.language.iso eng
dc.relation.ispartof Journal of Imaging, 2021, vol. 7, num. 186, p. 1-21
dc.rights.uri info:eu-repo/semantics/openAccess
dc.source Benouis, Mohamed Medus, Leandro Daniel Saban, Mohamed Ghemougui, Abdessattar Rosado Muñoz, Alfredo 2021 Food Tray Sealing Fault Detection in Multi-Spectral Images Using Data Fusion and Deep Learning Techniques Journal of Imaging 7 186 1 21
dc.subject Tecnologia dels aliments
dc.subject Envasos de plàstic
dc.subject Aliments Conservació
dc.title Food Tray Sealing Fault Detection in Multi-Spectral Images Using Data Fusion and Deep Learning Techniques
dc.type info:eu-repo/semantics/article
dc.date.updated 2022-04-11T18:36:02Z
dc.identifier.doi https://doi.org/10.3390/jimaging7090186
dc.identifier.idgrec 151387

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