Analysis of the Possibilities of Tire-Defect Inspection Based on Unsupervised Learning and Deep Learning.

Kuric, Ivan; Klarák, Jaromír; Sága, Milan; Císar, Miroslav; Hajducík, Adrián; Wiecek, Dariusz

Kuric, Ivan; Klarák, Jaromír; Sága, Milan; Císar, Miroslav; Hajducík, Adrián; Wiecek, Dariusz.

Afiliación

Kuric I; Department of Automation and Production Systems, Faculty of Mechanical Engineering, University of Zilina, 010 26 Zilina, Slovakia.
Klarák J; Department of Automation and Production Systems, Faculty of Mechanical Engineering, University of Zilina, 010 26 Zilina, Slovakia.
Sága M; Department of Automation and Production Systems, Faculty of Mechanical Engineering, University of Zilina, 010 26 Zilina, Slovakia.
Císar M; Department of Automation and Production Systems, Faculty of Mechanical Engineering, University of Zilina, 010 26 Zilina, Slovakia.
Hajducík A; Department of Automation and Production Systems, Faculty of Mechanical Engineering, University of Zilina, 010 26 Zilina, Slovakia.
Wiecek D; Faculty of Mechanical Engineering and Computer Science, ATH-University of Bielsko Biala, 43-309 Bielsko-Biala, Poland.

Sensors (Basel) ; 21(21)2021 Oct 25.

Article en En | MEDLINE | ID: mdl-34770379

Asunto(s)

Aprendizaje Profundo; Aprendizaje Automático no Supervisado; Algoritmos; Análisis por Conglomerados; Redes Neurales de la Computación

Palabras clave

deep learning; defect detection; laser sensor; polar transform; polynomial regression; tire inspection; unsupervised learning

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Aprendizaje Automático no Supervisado / Aprendizaje Profundo Idioma: En Revista: Sensors (Basel) Año: 2021 Tipo del documento: Article País de afiliación: Eslovaquia Pais de publicación: Suiza

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