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Flame and Smoke Detection in Substation Based on Wavelet Analysis and Convolution Neural Network

Authors:

Tian YuanAuthors Info & Claims

ICIAI '19: Proceedings of the 2019 3rd International Conference on Innovation in Artificial Intelligence

Pages 248 - 252

https://doi.org/10.1145/3319921.3319962

Published: 15 March 2019 Publication History

Abstract

In this paper, a fire detection method based on color features, wavelet analysis, and convolution neural network is proposed. Firstly, the candidate region of flame is extracted by color segmentation method, and then the candidate region of smoke is generated by the background fuzzy model based on wavelet analysis. Then the candidate region is filtered by the trained CNN model, and the position of flame and smoke in a picture is located. Finally, a large number of fire pictures in different scenes are used to test the algorithm. The results show that this method can detect the location of flame and smoke accurately and quickly from images or videos, and can be applied to fire detection tasks in substation scenarios.

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Cited By

Lin JFu CHuang QZhu Y(2024)Contextual Interaction Enhancement Network for Smoke Detection2024 IEEE International Conference on Multimedia and Expo (ICME)10.1109/ICME57554.2024.10688231(1-6)Online publication date: 15-Jul-2024
https://doi.org/10.1109/ICME57554.2024.10688231
Chan CAlvi SZhou XDurrani SWilson NYebra M(2024)A Survey on IoT Ground Sensing Systems for Early Wildfire Detection: Technologies, Challenges, and OpportunitiesIEEE Access10.1109/ACCESS.2024.350133612(172785-172819)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3501336
Gragnaniello DGreco ASansone CVento B(2024)Fire and smoke detection from videos: A literature review under a novel taxonomyExpert Systems with Applications10.1016/j.eswa.2024.124783255(124783)Online publication date: Dec-2024
https://doi.org/10.1016/j.eswa.2024.124783
Show More Cited By

Index Terms

Flame and Smoke Detection in Substation Based on Wavelet Analysis and Convolution Neural Network
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object detection

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cover image ACM Other conferences

ICIAI '19: Proceedings of the 2019 3rd International Conference on Innovation in Artificial Intelligence

March 2019

279 pages

ISBN:9781450361286

DOI:10.1145/3319921

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Xi'an Jiaotong-Liverpool University: Xi'an Jiaotong-Liverpool University
University of Texas-Dallas: University of Texas-Dallas

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 March 2019

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ICIAI 2019: 2019 The 3rd International Conference on Innovation in Artificial Intelligence

March 15 - 18, 2019

Suzhou, China

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Cited By

Lin JFu CHuang QZhu Y(2024)Contextual Interaction Enhancement Network for Smoke Detection2024 IEEE International Conference on Multimedia and Expo (ICME)10.1109/ICME57554.2024.10688231(1-6)Online publication date: 15-Jul-2024
https://doi.org/10.1109/ICME57554.2024.10688231
Chan CAlvi SZhou XDurrani SWilson NYebra M(2024)A Survey on IoT Ground Sensing Systems for Early Wildfire Detection: Technologies, Challenges, and OpportunitiesIEEE Access10.1109/ACCESS.2024.350133612(172785-172819)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3501336
Gragnaniello DGreco ASansone CVento B(2024)Fire and smoke detection from videos: A literature review under a novel taxonomyExpert Systems with Applications10.1016/j.eswa.2024.124783255(124783)Online publication date: Dec-2024
https://doi.org/10.1016/j.eswa.2024.124783
Ko YHamed Mozaffari MLi Y(2024)Fire and Smoke Image RecognitionIntelligent Building Fire Safety and Smart Firefighting10.1007/978-3-031-48161-1_13(305-333)Online publication date: 26-Jan-2024
https://doi.org/10.1007/978-3-031-48161-1_13
Wang JZhou HSun HSu ZLi X(2022)A Violation Behaviors Detection Method for Substation Operators based on YOLOv5 And Pose Estimation2022 IEEE 3rd China International Youth Conference on Electrical Engineering (CIYCEE)10.1109/CIYCEE55749.2022.9958961(1-5)Online publication date: 3-Nov-2022
https://doi.org/10.1109/CIYCEE55749.2022.9958961
Thai TLe QVy Nguyen CThao Nguyen MTuyen Nguyen XTri Phan GSang Nguyen TCuong Pham V(2022)Autonomous Mobile Robot: Navigating and Monitoring Fire Safety at Power Substations2022 7th National Scientific Conference on Applying New Technology in Green Buildings (ATiGB)10.1109/ATiGB56486.2022.9984088(177-182)Online publication date: 11-Nov-2022
https://doi.org/10.1109/ATiGB56486.2022.9984088
Chaturvedi SKhanna POjha A(2022)A survey on vision-based outdoor smoke detection techniques for environmental safetyISPRS Journal of Photogrammetry and Remote Sensing10.1016/j.isprsjprs.2022.01.013185(158-187)Online publication date: Mar-2022
https://doi.org/10.1016/j.isprsjprs.2022.01.013
Umoh UEyoh IMurugesan VNyoho E(2022)Fuzzy-machine learning models for the prediction of fire outbreaks: A comparative analysisArtificial Intelligence and Machine Learning for EDGE Computing10.1016/B978-0-12-824054-0.00025-3(207-233)Online publication date: 2022
https://doi.org/10.1016/B978-0-12-824054-0.00025-3
Nakip MGuzelis CYildiz O(2021)Recurrent Trend Predictive Neural Network for Multi-Sensor Fire DetectionIEEE Access10.1109/ACCESS.2021.30877369(84204-84216)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3087736
Geetha SAbhishek CAkshayanat C(2020)Machine Vision Based Fire Detection Techniques: A SurveyFire Technology10.1007/s10694-020-01064-z57:2(591-623)Online publication date: 27-Nov-2020
https://doi.org/10.1007/s10694-020-01064-z
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