File(s) not publicly available

A deep neural network and rule-based technique for fire risk identification in video frames

journal contribution
posted on 13.03.2020, 00:00 by Ligang Zhang, Brijesh Verma
Automatically monitoring roadside fire risk plays a significant role in ensuring road safety by reducing potential hazards imposed to vehicle drivers and enabling effective roadside vegetation management. However, little work has been conducted in this field using video data collected by vehicle-mounted cameras. In this paper, a novel approach is proposed for roadside fire risk identification based on the biomass of grasses. Inspired by the biomass measurement method by human in grass curing, the proposed approach predicts the biomass and identifies high-risk regions using threshold-based rules based on two site-specific parameters of roadside grasses—brown grass coverage (BGC) and height (BGH). The BGC is calculated as the percentage of brown grass pixels in a sampling region, while the BGH is predicted based on the connectivity characteristics of grass stems along the vertical direction. To further reduce the false alarm rate of fire risk, we additionally incorporate and compare two deep learning techniques, including autoencoder and convolutional neural network, for refining the results. Our approach shows high performance of combining threshold-based rules with deep neural networks in classifying low and high fire risk on a roadside image dataset from video collected by the Department of Transport and Main Roads, Queensland, Australia. © 2018, Springer-Verlag London Ltd., part of Springer Nature.

Funding

Category 1 - Australian Competitive Grants (this includes ARC, NHMRC)

History

Volume

22

Issue

1

Start Page

187

End Page

203

Number of Pages

17

eISSN

1433-755X

ISSN

1433-7541

Publisher

Springer, UK

Peer Reviewed

Yes

Open Access

No

Acceptance Date

29/10/2018

Author Research Institute

Centre for Intelligent Systems

Era Eligible

Yes

Journal

Pattern Analysis and Applications