Cargando…
Semi-supervised wildfire smoke detection based on smoke-aware consistency
The semi-transparency property of smoke integrates it highly with the background contextual information in the image, which results in great visual differences in different areas. In addition, the limited annotation of smoke images from real forest scenarios brings more challenges for model training...
Autores principales: | Wang, Chuansheng, Grau, Antoni, Guerra, Edmundo, Shen, Zhiguo, Hu, Jinxing, Fan, Haoyi |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9678925/ https://www.ncbi.nlm.nih.gov/pubmed/36426142 http://dx.doi.org/10.3389/fpls.2022.980425 |
Ejemplares similares
-
All-in-one aerial image enhancement network for forest scenes
por: Chen, Zhaoqi, et al.
Publicado: (2023) -
On the stratospheric chemistry of midlatitude wildfire smoke
por: Solomon, Susan, et al.
Publicado: (2022) -
Semi-supervised Learning for Weed and Crop Segmentation Using UAV Imagery
por: Nong, Chunshi, et al.
Publicado: (2022) -
The Effects of Wildfire Smoke on Asthma and Allergy
por: Noah, Terry L., et al.
Publicado: (2023) -
As California burns: the psychology of wildfire- and wildfire smoke-related migration intentions
por: Berlin Rubin, Nina, et al.
Publicado: (2022)