Food Tray Sealing Fault Detection in Multi-Spectral Images Using Data Fusion and Deep Learning Techniques
| Autor: | Abdessattar Ghemougui, Leandro D. Medus, Mohamed Benouis, Mohamed Saban, Alfredo Rosado-Muñoz |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Envasos de plàstic
Computer science hyperspectral imaging Computer applications to medicine. Medical informatics R858-859.7 Convolutional neural network Article Deep belief network Photography Radiology Nuclear Medicine and imaging Electrical and Electronic Engineering TR1-1050 Extreme learning machine Image fusion data fusion business.industry Deep learning Hyperspectral imaging deep learning Pattern recognition Aliments Conservació QA75.5-76.95 Sensor fusion Computer Graphics and Computer-Aided Design Autoencoder fault detection Electronic computers. Computer science Computer Vision and Pattern Recognition Artificial intelligence Tecnologia dels aliments business food packaging |
| Zdroj: | Journal of Imaging Volume 7 Issue 9 Journal of Imaging, Vol 7, Iss 186, p 186 (2021) 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 RODERIC. Repositorio Institucional de la Universitat de Valéncia instname |
| ISSN: | 2313-433X |
| DOI: | 10.3390/jimaging7090186 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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