Automating dairy production lines with the yoghurt cups recognition and detection process in the Industry 4.0 era
Fotios K. Konstantinidis, Vasiliki Balaska, Symeon Symeonidis, Dimitrios Tsilis, Spyridon G. Mouroutsos, Loukas Bampis, Athanasios Psomoulis, Αντώνιος Γαστεράτος
Abstract
The explosion of the digitisation of traditional industrial processes and procedures is consolidating a positive impact on modern society by offering a critical contribution to its economic development. In particular, the dairy sector consists of various processes, which are very demanding and thorough. Therefore, it is crucial to leverage modern automation tools and through-engineering solutions to increase efficiency and continuously meet challenging standards. Towards this end, an intelligent algorithm based on machine vision and artificial intelligence, which identifies dairy products within production lines, is presented in this work. Furthermore, to train and validate the model, the novel YogDATA dataset was created that includes yoghurt cups captured within a production line. Specifically, we evaluate two deep learning models (Mask R-CNN and YOLO v5.0) to recognise and detect each yoghurt cup in a production line to automate product packaging processes. According to our results, the performance precision of the two models is quite similar, fulfilling the identification requirement of Dairy 4.0 production systems.