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Multi-feature Fusion Flame Detection Algorithm Based on BP Neural Network

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Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD 2022)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 153))

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Abstract

In recent years, in order to ensure the safety of industrial boilers in production and improve the utilization rate of coal resources, a series of technical regulations on the detection of industrial boilers and related industrial emission regulations have been issued. In this paper, the traditional flame detection method has the problems of low accuracy, high failure rate and high maintenance cost caused by complicated detection equipment. A multi-feature fusion flame detection algorithm based on BP Neural Network is designed. For flame images with flickering characteristics, during the preprocessing of the data set, the principle of retaining more flame features is to use the sample matrix of four types of flame features, are used for training, and the proposed flame detection algorithm is applied to the actual flame sample test matrix to verify the timeliness of the algorithm proposed.

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Acknowledgments

Supported by Shaanxi Province Key Research and Development Project (2021GY-280, 2021GY-029); Shaanxi Province Natural Science Basic Research Program Project (2021JM-459).

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Authors and Affiliations

  1. School of Electronic Engineering, Xi’an University of Posts and Telecommunications, Xi’an, 710121, China

    Jin Wu, Ling Yang, Yaqiong Gao & Zhaoqi Zhang

Authors
  1. Jin Wu
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  2. Ling Yang
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  3. Yaqiong Gao
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  4. Zhaoqi Zhang
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Corresponding author

Correspondence to Jin Wu .

Editor information

Editors and Affiliations

  1. Division of Intelligent Future Technologies, Mälardalen University, Västerås, Västmanlands Län, Sweden

    Ning Xiong

  2. Department of Electronic and Computer Engineering, Brunel University London, Uxbridge, Middlesex, UK

    Maozhen Li

  3. School of Information Science and Technology, Hunan University, Changsha, Hunan, China

    Kenli Li

  4. School of Information Science and Technology, Hunan University, Changsha, Hunan, China

    Zheng Xiao

  5. College of Computer and Data Science, Fuzhou University, Fuzhou, Fujian, China

    Longlong Liao

  6. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Lipo Wang

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Wu, J., Yang, L., Gao, Y., Zhang, Z. (2023). Multi-feature Fusion Flame Detection Algorithm Based on BP Neural Network. In: Xiong, N., Li, M., Li, K., Xiao, Z., Liao, L., Wang, L. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 153. Springer, Cham. https://doi.org/10.1007/978-3-031-20738-9_45

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