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Quality control is a key process designed to ensure that only products satisfying the defined quality requirements reach the end consumer or the next step in a production line. In the food industry, in the packaging step, there are many products that are still evaluated by human operators. To automate the process and improve efficiency and effectiveness, computer vision and artificial intelligence techniques can be applied. This automation is challenging since specific strategies designed according to the application scenario are required. Focusing on the quality control of the sealing and closure of matrix-shaped thermoforming food packages, the aim of the article is to propose a deep-learning-based solution designed to automatically perform the quality control while satisfying production cadence and ensuring 100% inline inspection of the products. Particularly, the designed computer vision system and the image-based criteria defined to determine when a product has to be accepted or rejected are presented. In addition, the vision control software is described with special emphasis on the different convolutional neural network (CNN) architectures that have been considered (ResNet18, ResNet50, Vgg19 and DenseNet161, non-pre-trained and pre-trained on ImageNet) and on the specifically designed dataset. To test the solution, different experiments are carried out in the laboratory and also in a real scenario, concluding that the proposed CNN-based approach improves the efficiency and security of the quality control process. Optimal results are obtained with the pre-trained DenseNet161, achieving false positive rates that range from 0.03 to 0.30% and false negative rates that range from 0 to 0.07%, with a rejection rate between 0.64 and 5.09% of production, and being able to detect at least 99.93% of the sealing defects that occur in any production. The modular design of our solution as well as the provided description allow it to adapt to similar scenarios and to new deep-learning models to prevent the arrival of faulty products to end consumers by removing them from the automated production line.

The aim of this study was to determine whether e-learning as a new teaching methodology was acceptable for general practitioners in continuous education courses of radiology. Generally, these courses are face-to-face with the corresponding time and place limitations. To overcome these limitations, we transformed one of these courses to an online one evaluating its acceptance. The course was about thorax radiology and it was delivered to 249 participants. The experiment was carried out in two phases: Phase 1, as a pilot testing with 12 general practitioners (G1), and Phase 2, with 149 general practitioners (G2), 12 radiologists (G3) and 76 medical residents (G4). All participants evaluated the course design, the delivering e-learning platform, and the course contents using a five-point Likert scale (satisfaction level from 1 to 5). Collected data was analysed using t, Mann-Whitney U and Kruskal-Wallis tests. In Phase 1, the rounded scores of all questions except one surpassed 3.5. In Phase 2, all the rounded scores surpassed 4.0 indicating that a total agreement on all items was achieved. All collected impressions indicate the high acceptance of the proposed methodology.

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