Depth Data Denoising in Optical Laser Based Sensors for Metal Sheet Flatness Measurement: A Deep Learning Approach.

Alonso, Marcos; Maestro, Daniel; Izaguirre, Alberto; Andonegui, Imanol; Graña, Manuel

Alonso, Marcos; Maestro, Daniel; Izaguirre, Alberto; Andonegui, Imanol; Graña, Manuel.

Afiliación

Alonso M; Robotics and Automation Group, Electronic and Computer Science Department, Faculty of Engineering, Mondragon University, Loramendi Kalea, 4, 20500 Arrasate-Mondragon, Spain.
Maestro D; Computational Intelligence Group, CCIA Department, UPV/EHU, Paseo Manuel de Lardizabal 1, 20018 San Sebastian, Spain.
Izaguirre A; Robotics and Automation Group, Electronic and Computer Science Department, Faculty of Engineering, Mondragon University, Loramendi Kalea, 4, 20500 Arrasate-Mondragon, Spain.
Andonegui I; Robotics and Automation Group, Electronic and Computer Science Department, Faculty of Engineering, Mondragon University, Loramendi Kalea, 4, 20500 Arrasate-Mondragon, Spain.
Graña M; Robotics and Automation Group, Electronic and Computer Science Department, Faculty of Engineering, Mondragon University, Loramendi Kalea, 4, 20500 Arrasate-Mondragon, Spain.

Sensors (Basel) ; 21(21)2021 Oct 23.

Article en En | MEDLINE | ID: mdl-34770331

Palabras clave

Convolutional Neural Networks; deep learning; depth data denoising; laser triangulation; metal sheet flatness measurement; residual learning; smooth surface reconstruction

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Clinical_trials Idioma: En Revista: Sensors (Basel) Año: 2021 Tipo del documento: Article País de afiliación: España Pais de publicación: Suiza

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