Application of transformer nets for discrimination of liquid cleaning products
DOI:
https://doi.org/10.53591/easi.v4i2.2600Keywords:
Artificial intelligence, Discrimination of products, Labeling, Transformer neural networksAbstract
This paper presents an artificial intelligence algorithm based on transformer neural networks that allows the discrimination of liquid products identified with different labels and with presentations in various colors, from a camera, to facilitate the management of their subsequent handling by a computer, which allows us to realize, in manufacturing environments, the connection between the physical and the digital world. The process begins with the digitalization of the products to establish a database. Next, the parameters of the network training are defined, which are then evaluated by measuring the learning time, accuracy, and classification time, all of which are developed in a virtual environment. Thanks to the results, it is possible to conclude that even with a small amount of data, including label images that are not complete or of the best quality, the processing times do not exceed 0.5 seconds. A recognition rate of 100% accuracy is achieved, corresponding to the absence of confusion between the considered categories, given the robustness of the selected transformer network.
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