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

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.

Advances in Machine Learning and Hyperspectral Imaging in the Food Supply Chain

“…PCANet is mainly used for spatial and spectral data fusion and output feature vectors, KNN and SVM as output classifiers with the accuracy of 89% and 90%, respectively, and there is space for improvement in both accuracy and detection speed. Benouis et al [92] proposed a deep learning-based fault detection method for food tray sealing to improve the model's generalization ability. To reduce the amount of data for training the network, two pixel-level fusion algorithms, spatial and transform image fusion, were used in the study to process the data.…”

A chemometric approach to assess the oil composition and content of microwave-treated mustard (Brassica juncea) seeds using Vis–NIR–SWIR hyperspectral imaging

“…HSI, in particular, offers several advantages over other techniques such as NIR spectroscopy, multispectral imaging, and conventional RGB imaging. These advantages include the capability to gather spatial and spectral data as well as heightened sensitivity for even the smallest components 15 .…”

Proceedings - The Eleventh International Symposium GRID 2022

“…Multispectral and hyperspectral images are helpful in many fields, e.g. in geology (Ninomiya & Fu, 2019), agriculture (Hassan-Esfahani et al, 2014), food industry (Benouis et al, 2021), healthcare and cultural heritage (Colantonio et al, 2018). Research in the field of multi-and hyperspectral image capture is useful and current, as well as, due to the affordability of high-resolution commercial cameras, also research that uses various techniques of converting RGB to multidimensional spectral image space.…”