Cargando…
Defect Inspection Using Modified YoloV4 on a Stitched Image of a Spinning Tool
In Industry 4.0, automation is a critical requirement for mechanical production. This study proposes a computer vision-based method to capture images of rotating tools and detect defects without the need to stop the machine in question. The study uses frontal lighting to capture images of the rotati...
Autores principales: | Lin, Bor-Haur, Chen, Ju-Chin, Lien, Jenn-Jier James |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10181639/ https://www.ncbi.nlm.nih.gov/pubmed/37177683 http://dx.doi.org/10.3390/s23094476 |
Ejemplares similares
-
Intelligent Tapping Machine: Tap Geometry Inspection
por: Lin, En-Yu, et al.
Publicado: (2023) -
MSFT-YOLO: Improved YOLOv5 Based on Transformer for Detecting Defects of Steel Surface
por: Guo, Zexuan, et al.
Publicado: (2022) -
MR-YOLO: An Improved YOLOv5 Network for Detecting Magnetic Ring Surface Defects
por: Lang, Xianli, et al.
Publicado: (2022) -
Platelet Detection Based on Improved YOLO_v3
por: Liu, Renting, et al.
Publicado: (2022) -
Nut Geometry Inspection Using Improved Hough Line and Circle Methods
por: Lin, En-Yu, et al.
Publicado: (2023)