Citation

Wahyono, Dharmawan A, Harjoko A, Chrystian, Adhinata FD. Data Brief 2022; 41: 107925.

Copyright

(Copyright © 2022, Elsevier Publishing)

DOI

10.1016/j.dib.2022.107925

PMID

35198696

PMCID

PMC8847809

Abstract

This paper presents fire segmentation annotation data on 12 commonly used and publicly available "VisiFire Dataset" videos from http://signal.ee.bilkent.edu.tr/VisiFire/. This annotations dataset was obtained by per-frame, manual hand annotation over the fire region with 2684 total annotated frames. Since this annotation provides per-frame segmentation data, it offers a new and unique fire motion feature to the existing video, unlike other fire segmentation data that are collected from different still images. The annotations dataset also provides ground truth for segmentation task on videos. With segmentation task, it offers better insight on how well a machine learning model understood, not only detecting whether a fire is present, but also its exact location by calculating metrics such as Intersection over Union (IoU) with this annotations data. This annotations data is a tremendously useful addition to train, develop, and create a much better smart surveillance system for early detection in high-risk fire hotspots area.

Language: en

Keywords

Computer vision; Segmentation; Fire; Intelligent surveillance system