Benouis Mohamed, Medus Leandro D, Saban Mohamed, Ghemougui Abdessattar, Rosado-Muñoz Alfredo
Laboratory of Informatics and Its Applications of M'sila (LIAM), Department of Computer Science, University of M'Sila, BP 166 Ichbilia, Msila 28000, Algeria.
Department of Electronic Engineering, ETSE, University Valencia, Av. Universitat, s/n-46100, Burjassot, 46100 Valencia, Spain.
J Imaging. 2021 Sep 16;7(9):186. doi: 10.3390/jimaging7090186.
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.
为了保持食品特性并确保消费者安全，需要正确密封食品托盘。传统的食品包装检查由人工操作员进行，以检测密封缺陷。食品检测领域的最新进展与高光谱成像技术和基于视觉的自动检测系统的使用有关。本文描述了一种基于深度学习的方法，用于使用高光谱图像检测食品托盘密封故障。提出了几种基于像素的图像融合方法，以从三维高光谱图像数据立方体中获取二维图像，这些二维图像将输入深度学习（DL）算法。通过数据融合，在受污染或有故障的密封区域周围的感兴趣区域中，不考虑所有光谱带，而是仅选择相关光谱带。这些技术在保持高分类率的同时，大大缩短了计算时间，表明融合后的图像包含足够的信息来检查食品托盘的密封状态（有故障或正常），避免将大型图像数据立方体输入到DL算法中。此外，所提出的DL算法不需要任何先验的手工方法，即由于训练过程会调整算法，因此不需要手动调整算法中的参数。使用食品托盘工业数据集以及不同的深度学习方法进行验证的实验结果证明了所提方法的有效性。在研究的数据集中，深度信念网络（DBN）、极限学习机（ELM）、堆叠自动编码器（SAE）和卷积神经网络（CNN）的准确率分别达到了88.7%、88.3%、89.3%和90.1%。
