Deep learning for the quality control of thermoforming food packages

Abstract

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.

An Investigation in Analyzing the Food Quality Well-Being for Lung Cancer Using Blockchain through CNN

“…Scientists have determined that in order to deal with around 1.9 million new lung cancer cases globally, it is important to undertake the help of blockchain materials for applying the CNN method [5]. It has been recorded that around 13% of all critical lung cancer cases can be detected and diagnosed by using this blockchain technical approach [6]. Lung cancer is a worldwide burning issue that is increasing rapidly.…”

MOSPPA: monitoring system for palletised packaging recognition and tracking

“…In [26], meanwhile, background subtraction and segmentation is used to detect foreign materials (metal, wood, rubber, etc. ), and in [27], ResNet, VGG and DenseNet architectures are used to detect imperfections in thermoforming food packages. Although these works are not focused on pallet detection, the approaches used are similar to that of our proposal, since they address object detection and instance-level recognition in different fields and industries.…”

A Prestudy of Machine Learning in Industrial Quality Control Pipelines

“…Related to food, [1], explore the usefulness of using machine vision in food packaging. Specifically, they analyze the seals of thermoforming packaging using three convolutional neural networks (CNNs): ResNet [6], VGG [16], DenseNet [8].…”

“…Results [5] Using feature elimination and classification threshold search to find best models for classifying good or bad welds Logistic regression model -maximum probability of correct decision (MPCD): 1 [12] Testing several machine learning models to predict dimensional defects in cars Both XGBoost and Random Forest -ROC AUC: 0.97 [14] Training machine learning models to detect defective units during PCB manufacturing Gradient boosted tree -precision: 93.1%, recall: 89.9%, accuracy: 92.6% [1] Proper seal detection in thermoforming food packaging ResNet18 -accuracy: 95% [11] Convolutional neural networks in detecting faults in cast metal products Custom CNN -accuracy: 99.82% The small number of learning examples was insufficient for a hard real-life problem, as expected.…”