# A Computer Vision-Based Quality Assessment Technique for the automatic control of consumables for analytical laboratories

arXiv: https://arxiv.org/abs/2404.10454 · HTML: https://mlsanity.com/paper/2404.10454/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 102 references that are in the map.
- First posted on arXiv 16 Apr 2024.

## Builds on (most cited first, 16 of 16)

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- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
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- [The Effectiveness of Data Augmentation in Image Classification using Deep Learning](https://mlsanity.com/paper/1712.04621/) (2017)
- [Why do deep convolutional networks generalize so poorly to small image transformations?](https://mlsanity.com/paper/1805.12177/) (2018)
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- [The Theory Behind Overfitting, Cross Validation, Regularization, Bagging, and Boosting: Tutorial](https://mlsanity.com/paper/1905.12787/) (2019)
- [Further advantages of data augmentation on convolutional neural networks](https://mlsanity.com/paper/1906.11052/) (2019)
- [Adaptive Weight Decay for Deep Neural Networks](https://mlsanity.com/paper/1907.08931/) (2019)
- [A Preliminary Study on Data Augmentation of Deep Learning for Image Classification](https://mlsanity.com/paper/1906.11887/) (2019)
- [DeepWaste: Applying Deep Learning to Waste Classification for a Sustainable Planet](https://mlsanity.com/paper/2101.05960/) (2021)

## Abstract (arXiv)

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.