A Deep Learning-Based Approach for Quality Control and Defect Detection for Industrial Bagging Systems

In the competitive world of the food industry where companies have to offer quality products, quality control is essential. However, it could become expensive, especially if it is a manual process. Its automation then becomes an excellent opportunity for a company. The objective of this research is to find out whether it is possible to carry out quality control of open mouth hag sealings on industrial bagging systems using deep learning. In this paper, we propose a three-step approach: data collection, data classification, and supervised classification learning. The first step is to collect images of sealings of open mouth bags. We created a line-scan based prototype and placed it on a production line to harvest a large amount of data. Image processing is then applied to clean the data. The next step is the classification of the data to identify classes of defects and labeling of these data. Finally, supervised classification learning makes it possible to implement quality control. We propose an architecture based on convolutional neural networks for image classification of open mouth bags. Our approach gives very encouraging results for the realization of quality control of an industrial bagging system.