BLSTM based night-time wildfire detection from video.
Distinguishing fire from non-fire objects in night videos is problematic if only spatial features are to be used. Those features are highly disrupted under low-lit environments because of several factors, such as the dynamic range limitations of the cameras. This makes the analysis of temporal behav...
Saved in:
| Main Authors: | Ahmet K Agirman, Kasim Tasdemir |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0269161&type=printable |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Deep Learning-Based Efficient Model Development for Phishing Detection Using Random Forest and BLSTM Classifiers
by: Shan Wang, et al.
Published: (2020-01-01) -
Assessing the effectiveness of transfer learning strategies in BLSTM networks for speech denoising
by: Marvin Coto-Jiménez, et al.
Published: (2022-11-01) -
Recognition Algorithm of AE Signal of Rock Fracture Based on Multiscale 1DCNN-BLSTM
by: Weihua Wang
Published: (2024-01-01) -
Bangla Speech Emotion Recognition and Cross-Lingual Study Using Deep CNN and BLSTM Networks
by: Sadia Sultana, et al.
Published: (2022-01-01) -
Syntactic complexity recognition and analysis in Chinese-English machine translation: A comparative study based on the BLSTM-CRF model.
by: Yongli Tian
Published: (2025-01-01)