Descripci\'on autom\'atica de secciones delgadas de rocas: una aplicaci\'on Web
<|reference_start|>Descripci\'on autom\'atica de secciones delgadas de rocas: una aplicaci\'on Web: The identification and characterization of various rock types is one of the fundamental activities for geology and related areas such as mining, petroleum, environment, industry and construction. Traditionally, a human specialist is responsible for analyzing and explaining details about the type, composition, texture, shape and other properties using rock samples collected in-situ or prepared in a laboratory. The results become subjective based on experience, in addition to consuming a large investment of time and effort. The present proposal uses artificial intelligence techniques combining computer vision and natural language processing to generate a textual and verbal description from a thin section image of rock. We build a dataset of images and their respective textual descriptions for the training of a model that associates the relevant features of the image extracted by EfficientNetB7 with the textual description generated by a Transformer network, reaching an accuracy value of 0.892 and a BLEU value of 0.71. This model can be a useful resource for research, professional and academic work, so it has been deployed through a Web application for public use.<|reference_end|>
@article{paucar2024descripci\'on,
    title={Descripci\'on autom\'atica de secciones delgadas de rocas: una
  aplicaci\'on Web},
    author={Stalyn Paucar, Christian Mej'ia-Escobar y V'ictor Collaguazo},
    journal={arXiv preprint arXiv:2402.15039},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.15039},
    primaryClass={cs.CV cs.LG}
}