Short Circuit Recognition for Metal Electrorefining Using an Improved Faster R-CNN With Synthetic Infrared Images
This paper is concerned with the problem of short circuit detection in infrared image for metal electrorefining with an improved Faster Region-based Convolutional Neural Network (Faster R-CNN). To address the problem of insufficient label data, a framework for automatically generating labeled infrar...
Guardado en:
Autores principales: | Xin Li, Yonggang Li, Renchao Wu, Can Zhou, Hongqiu Zhu |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Frontiers Media S.A.
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/95510d06f88a4b78b72ff842179557a0 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Pulmonary Nodule Detection Based on Faster R-CNN With Adaptive Anchor Box
por: Chi Cuong Nguyen, et al.
Publicado: (2021) -
Copper Cathode Contamination by Nickel in Copper Electrorefining
por: Mika Sahlman, et al.
Publicado: (2021) -
Klasifikasi Pola Kain Tenun Melayu Menggunakan Faster R-CNN
por: Yoze Rizki, et al.
Publicado: (2021) -
Reliability Enhancement of Power IGBTs under Short-Circuit Fault Condition Using Short-Circuit Current Limiting-Based Technique
por: Sadegh Mohsenzade, et al.
Publicado: (2021) -
Transfer Learning-Based Algorithms for the Detection of Fatigue Crack Initiation Sites: A Comparative Study
por: S.Y. Wang, et al.
Publicado: (2021)