short-paper

Research on Smoke Detection based on DenseNet

Authors:

XiQuan Yang,

Ying SunAuthors Info & Claims

ACMSE '19: Proceedings of the 2019 ACM Southeast Conference

Pages 160 - 163

https://doi.org/10.1145/3299815.3314449

Published: 18 April 2019 Publication History

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Abstract

Aiming at the difficulty of video smoke detection in complex scene, this paper proposes a method based on DenseNet to identify smoke. By extracting the color features of smoke and the property of upward movement of smoke, the method trains data through dense convolutional neural networks to learn the features between pictures. Experimental results show that the dense convolution neural network model can be effectively applied to real-time detection of smoke events in complex video scenarios.

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Cited By

View all

Wang YPiao YWang QWang HQi NZhang H(2024)Early Smoke Recognition Algorithm for Forest FiresForests10.3390/f1507108215:7(1082)Online publication date: 22-Jun-2024
https://doi.org/10.3390/f15071082
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

Index Terms

Research on Smoke Detection based on DenseNet
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Computer graphics
    1. Image manipulation
      1. Image processing

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Information & Contributors

Information

Published In

ACMSE '19: Proceedings of the 2019 ACM Southeast Conference

April 2019

295 pages

ISBN:9781450362511

DOI:10.1145/3299815

Conference Chair:
Dan Lo
Kennesaw State University
,
Program Chair:
Donghyun Kim
Kennesaw State University
,
Publications Chair:
Eric Gamess
Jacksonville State University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 April 2019

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Short-paper
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ACM SE '19

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ACM SE '19: 2019 ACM Southeast Conference

April 18 - 20, 2019

GA, Kennesaw, USA

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Overall Acceptance Rate 502 of 1,023 submissions, 49%

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Cited By

View all

Wang YPiao YWang QWang HQi NZhang H(2024)Early Smoke Recognition Algorithm for Forest FiresForests10.3390/f1507108215:7(1082)Online publication date: 22-Jun-2024
https://doi.org/10.3390/f15071082
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

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Cited By

Index Terms

Recommendations

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

Recurrent convolutional network for video-based smoke detection

Fire smoke detection algorithm based on motion characteristic and convolutional neural networks