dor_id: 4149149

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336.#.#.3: Artículo de Investigación

336.#.#.a: Artículo

351.#.#.6: https://jart.icat.unam.mx/index.php/jart

351.#.#.b: Journal of Applied Research and Technology

351.#.#.a: Artículos

harvesting_group: RevistasUNAM

270.1.#.p: Revistas UNAM. Dirección General de Publicaciones y Fomento Editorial, UNAM en revistas@unam.mx

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270.#.#.d: MX

270.1.#.d: México

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856.4.0.u: https://jart.icat.unam.mx/index.php/jart/article/view/1930/1013

100.1.#.a: Lin, Hong Dar; Lo, Yuan Chin; Lin, Chou Hsien

524.#.#.a: Lin, Hong Dar, et al. (2023). Vision based deformation inspection system for automotive glass using Hough circle detectors. Journal of Applied Research and Technology; Vol. 21 Núm. 4, 2023; 598-612. Recuperado de https://repositorio.unam.mx/contenidos/4149149

245.1.0.a: Vision based deformation inspection system for automotive glass using Hough circle detectors

502.#.#.c: Universidad Nacional Autónoma de México

561.1.#.a: Instituto de Ciencias Aplicadas y Tecnología, UNAM

264.#.0.c: 2023

264.#.1.c: 2023-08-31

653.#.#.a: Automotive glass; deformation inspection; computer vision; circular Hough transform; quality control

506.1.#.a: La titularidad de los derechos patrimoniales de esta obra pertenece a las instituciones editoras. Su uso se rige por una licencia Creative Commons BY-NC-ND 4.0 Internacional, https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode.es, para un uso diferente consultar al responsable jurídico del repositorio por medio del correo electrónico gabriel.ascanio@icat.unam.mx

884.#.#.k: https://jart.icat.unam.mx/index.php/jart/article/view/1930

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041.#.7.h: eng

520.3.#.a: Production of automotive glass with the accurate shape and form is a challenge that fabricates the glass products with properties of adequate transparency and lack of imaging deformations and optical flaws. An auto windscreen with deformation flaws will likely distort the driver’s view of surrounding objects, leading to errors in visual judgment that may be dangerous to other road users. In the traditional method of examining vehicle glass in the manufacturing process, human inspectors perform the bulk of the work. This study proposes a frequency reconstruction method established on computer vision to automatically detect deformation flaws in automotive glass. To quantify the deformation level of an outwardly curving glass product, we exploit the digital imaging of a known standard pattern with base dots through a testing sample to capture a transmitted and reflected deformation image of that sample. Then, the proposed method applies the circular Hough transform voting scheme to find the peak points of the base dots in parameter space and reconstructs an image with the base dots of the captured image. The binary testing image subtracts the binary reconstructed image to obtain a binary difference image that displays the detected deformation areas. Experimental outcomes present that the proposed approach using dots pattern reaches a high 82.76% probability of exactly discriminating deformation flaws and a low 1.14% probability of wrongly investigating regular regions as deformation flaws on transmitted appearances of transpicuous glass.

773.1.#.t: Journal of Applied Research and Technology; Vol. 21 Núm. 4 (2023); 598-612

773.1.#.o: https://jart.icat.unam.mx/index.php/jart

022.#.#.a: ISSN electrónico: 2448-6736; ISSN: 1665-6423

310.#.#.a: Bimestral

300.#.#.a: Páginas: 598-612

264.#.1.b: Instituto de Ciencias Aplicadas y Tecnología, UNAM

doi: https://doi.org/10.22201/icat.24486736e.2023.21.4.1930

harvesting_date: 2023-11-08 13:10:00.0

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Artículo

Vision based deformation inspection system for automotive glass using Hough circle detectors

Lin, Hong Dar; Lo, Yuan Chin; Lin, Chou Hsien

Instituto de Ciencias Aplicadas y Tecnología, UNAM, publicado en Journal of Applied Research and Technology, y cosechado de Revistas UNAM

Licencia de uso

Procedencia del contenido

Cita

Lin, Hong Dar, et al. (2023). Vision based deformation inspection system for automotive glass using Hough circle detectors. Journal of Applied Research and Technology; Vol. 21 Núm. 4, 2023; 598-612. Recuperado de https://repositorio.unam.mx/contenidos/4149149

Descripción del recurso

Autor(es)
Lin, Hong Dar; Lo, Yuan Chin; Lin, Chou Hsien
Tipo
Artículo de Investigación
Área del conocimiento
Ingenierías
Título
Vision based deformation inspection system for automotive glass using Hough circle detectors
Fecha
2023-08-31
Resumen
Production of automotive glass with the accurate shape and form is a challenge that fabricates the glass products with properties of adequate transparency and lack of imaging deformations and optical flaws. An auto windscreen with deformation flaws will likely distort the driver’s view of surrounding objects, leading to errors in visual judgment that may be dangerous to other road users. In the traditional method of examining vehicle glass in the manufacturing process, human inspectors perform the bulk of the work. This study proposes a frequency reconstruction method established on computer vision to automatically detect deformation flaws in automotive glass. To quantify the deformation level of an outwardly curving glass product, we exploit the digital imaging of a known standard pattern with base dots through a testing sample to capture a transmitted and reflected deformation image of that sample. Then, the proposed method applies the circular Hough transform voting scheme to find the peak points of the base dots in parameter space and reconstructs an image with the base dots of the captured image. The binary testing image subtracts the binary reconstructed image to obtain a binary difference image that displays the detected deformation areas. Experimental outcomes present that the proposed approach using dots pattern reaches a high 82.76% probability of exactly discriminating deformation flaws and a low 1.14% probability of wrongly investigating regular regions as deformation flaws on transmitted appearances of transpicuous glass.
Tema
Automotive glass; deformation inspection; computer vision; circular Hough transform; quality control
Idioma
eng
ISSN
ISSN electrónico: 2448-6736; ISSN: 1665-6423

Enlaces