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Análisis comparativo de técnicas de segmentación de estructuras reticulares


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Título :
Análisis comparativo de técnicas de segmentación de estructuras reticulares
Autor :
Soler, F. J.
Peidro, Adrian  
Fabregat Jaén, Marc
Paya, Luis  
Reinoso, Oscar
Editor :
UNIVERSIDAD DE ZARAGOZA ESCUELA DE INGENIERÍA Y ARQUITECTURA
Departamento:
Departamentos de la UMH::Ingeniería de Sistemas y Automática
Fecha de publicación:
2023-09
URI :
https://hdl.handle.net/11000/31586
Resumen :
This article aims to compare our previous work on segmentation of reticular structures with neural networks against an ad hoc algorithm for the same purpose. Nowadays neural networks or artificial intelligence are widely used concepts synonymous with advances and improvements, but in certain cases it is possible to use more classical techniques, outside the paradigm of artificial intelligence to achieve the same type of tasks with similar results. To corroborate last mention, in this article we perform a quantitative and qualitative comparative analysis between an ad hoc algorithm and the best neural network model in our latest work for segmenting reticular structures. Conventional methods such as Random Sample Consensus (RANSAC) and region growing are used to implement the algorithm. Standardised metrics such as precision, recall and f1-score are used for quantitative comparison. The latter will be calculated on a proprietary dataset, consisting of a thousand point clouds automatically generated in previous work. The algorithm in question is designed specifically for such a database
Palabras clave/Materias:
Region Growing
RANSAC
Plane Segmentation
Neural Networks
Point Clouds
Climbing Robots
Área de conocimiento :
CDU: Ciencias aplicadas: Ingeniería. Tecnología
Tipo documento :
application/pdf
Derechos de acceso:
info:eu-repo/semantics/openAccess
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
DOI :
https://doi.org/10.17979/spudc.9788497498609
Aparece en las colecciones:
Artículos Ingeniería de Sistemas y Automática



Creative Commons La licencia se describe como: Atribución-NonComercial-NoDerivada 4.0 Internacional.