  The rapid growth of the Industry 4.0 paradigm is increasing the pressure to
develop effective automated monitoring systems. Artificial Intelligence (AI) is
a convenient tool to improve the efficiency of industrial processes while
reducing errors and waste. In fact, it allows the use of real-time data to
increase the effectiveness of monitoring systems, minimize errors, make the
production process more sustainable, and save costs. In this paper, a novel
automatic monitoring system is proposed in the context of production process of
plastic consumables used in analysis laboratories, with the aim to increase the
effectiveness of the control process currently performed by a human operator.
In particular, we considered the problem of classifying the presence or absence
of a transparent anticoagulant substance inside test tubes. Specifically, a
hand-designed deep network model is used and compared with some
state-of-the-art models for its ability to categorize different images of vials
that can be either filled with the anticoagulant or empty. Collected results
indicate that the proposed approach is competitive with state-of-the-art models
in terms of accuracy. Furthermore, we increased the complexity of the task by
training the models on the ability to discriminate not only the presence or
absence of the anticoagulant inside the vial, but also the size of the test
tube. The analysis performed in the latter scenario confirms the
competitiveness of our approach. Moreover, our model is remarkably superior in
terms of its generalization ability and requires significantly fewer resources.
These results suggest the possibility of successfully implementing such a model
in the production process of a plastic consumables company.
