Cargando…
Comparison of Pre-Trained YOLO Models on Steel Surface Defects Detector Based on Transfer Learning with GPU-Based Embedded Devices
Steel is one of the most basic ingredients, which plays an important role in the machinery industry. However, the steel surface defects heavily affect its quality. The demand for surface defect detectors draws much attention from researchers all over the world. However, there are still some drawback...
Autores principales: | Nguyen, Hoan-Viet, Bae, Jun-Hee, Lee, Yong-Eun, Lee, Han-Sung, Kwon, Ki-Ryong |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9783860/ https://www.ncbi.nlm.nih.gov/pubmed/36560304 http://dx.doi.org/10.3390/s22249926 |
Ejemplares similares
-
MSFT-YOLO: Improved YOLOv5 Based on Transformer for Detecting Defects of Steel Surface
por: Guo, Zexuan, et al.
Publicado: (2022) -
EFC-YOLO: An Efficient Surface-Defect-Detection Algorithm for Steel Strips
por: Li, Yanshun, et al.
Publicado: (2023) -
A Novel ST-YOLO Network for Steel-Surface-Defect Detection
por: Ma, Hongtao, et al.
Publicado: (2023) -
Fast CNN Stereo Depth Estimation through Embedded GPU Devices
por: Aguilera, Cristhian A., et al.
Publicado: (2020) -
WD-YOLO: A More Accurate YOLO for Defect Detection in Weld X-ray Images
por: Pan, Kailai, et al.
Publicado: (2023)