Computer vision for wildfire research: An evolving image dataset for processing and analysis

The last decade has witnessed the use of computer vision for wildfire detection and measurement. The first and most important step for computer vision analysis is the fire pixel detection because it determines the accuracy of the following processing. The evaluation and the comparison of the wildfir...

Full description

Saved in:
Bibliographic Details
Published in:Fire safety journal Vol. 92; pp. 188 - 194
Main Authors: Toulouse, Tom, Rossi, Lucile, Campana, Antoine, Celik, Turgay, Akhloufi, Moulay A.
Format: Journal Article
Language:English
Published: Lausanne Elsevier Ltd 01.09.2017
Elsevier BV
Elsevier
Subjects:
algorithms
Annotations
Computer Science
Computer vision
Computer Vision and Pattern Recognition
data collection
Datasets
Evolution
Forest & brush fires
Forest fire detection
Ground truth
Image Processing
Pixel detection
Studies
Wildfire
Wildfires
Pixel detection
Database
Wildfire
Image
image
pixel detection
wildfire
ISSN:0379-7112, 1873-7226
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The last decade has witnessed the use of computer vision for wildfire detection and measurement. The first and most important step for computer vision analysis is the fire pixel detection because it determines the accuracy of the following processing. The evaluation and the comparison of the wildfire detection algorithms of the literature and the development of new ones needs open datasets with a large number of annotated images and their ground truth. We address this issue by presenting a publicly evolving wildfire annotated image database with ground truth data with examples of use. Currently, it contains 500 visible images and, in a more limited number, multimodal images and videos with frame by frame annotations. This is currently the largest dataset released in this research field.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:0379-7112
1873-7226
DOI:10.1016/j.firesaf.2017.06.012