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[期刊论文]

Theoretical and experimental study on image noise reduction for improving camera-based fire detection performance in thermal environments

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author:

Chen, Ming (Chen, Ming.) [1] | Chen, Kexin (Chen, Kexin.) [2] | Liu, Chunxiang (Liu, Chunxiang.) [3] (Scholars:刘春祥) | Unfold

Indexed by:

EI Scopus SCIE

Abstract:

Fire is one of the most common hazards in the process industry. Timely and accurate fire detection is essential. The camera-based technics for fire detection are one of the promising technologies. However, its uncertainty of fire monitoring quality, such as noise artifacts within digital images caused by the inherent interference of hot environments, is always a key defect hindering the further application of this technology. Taking a simple fire scenario of the cable fire as an example, the noise reduction model (SA-DCGAN, Spatial Attention-Deep Convolution Generative Adversarial Network) is discussed for three kinds of typical fire image noise (white, black and mottled). Compared with traditional noise reduction algorithm, the model has greater advantages in restoring flame profile and texture. Through the verification process of applying this method in promoting fire detection based on image recognition, the effectiveness of the theoretical model is confirmed in improving the detection accuracy. It shows that the "True Detection" is increased by 375% and the "Missed Detection" and "False Detection" are decreased by 54% and 587%, respectively. These results show that the proposed theoretical model is of great significance for improving camera-based fire detection performance in thermal environments, which makes possible to further promote the intelligent fire protection in the process industry.

Keyword:

AI technology Fire detection Fire image Image recognition Neural network Noise reduction

Community:

  • [ 1 ] [Chen, Ming]Fuzhou Univ, Coll Environm & Safety Engn, 2 Xue Yuan Rd, Fuzhou 350108, Peoples R China
  • [ 2 ] [Chen, Kexin]Fuzhou Univ, Coll Environm & Safety Engn, 2 Xue Yuan Rd, Fuzhou 350108, Peoples R China
  • [ 3 ] [Liu, Chunxiang]Fuzhou Univ, Coll Environm & Safety Engn, 2 Xue Yuan Rd, Fuzhou 350108, Peoples R China
  • [ 4 ] [Huang, Ping]Fuzhou Univ, Coll Environm & Safety Engn, 2 Xue Yuan Rd, Fuzhou 350108, Peoples R China
  • [ 5 ] [Yu, Longxing]Fuzhou Univ, Coll Environm & Safety Engn, 2 Xue Yuan Rd, Fuzhou 350108, Peoples R China
  • [ 6 ] [Yu, Longxing]Univ Sci & Technol China, State Key Lab Fire Sci, JinZhai Rd 96, Hefei 230026, Anhui, Peoples R China

Reprint 's Address:

  • [Huang, Ping]Fuzhou Univ, Coll Environm & Safety Engn, 2 Xue Yuan Rd, Fuzhou 350108, Peoples R China;;[Yu, Longxing]Fuzhou Univ, Coll Environm & Safety Engn, 2 Xue Yuan Rd, Fuzhou 350108, Peoples R China;;[Yu, Longxing]Univ Sci & Technol China, State Key Lab Fire Sci, JinZhai Rd 96, Hefei 230026, Anhui, Peoples R China;;

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Related Article:

Source :

JOURNAL OF THERMAL ANALYSIS AND CALORIMETRY

ISSN: 1388-6150

Year: 2022

Issue: 3

Volume: 148

Page: 1191-1199

4 . 4

JCR@2022

3 . 0 0 0

JCR@2023

ESI Discipline: CHEMISTRY;

ESI HC Threshold:74

JCR Journal Grade:1

CAS Journal Grade:3

Cited Count:

WoS CC Cited Count: 3

SCOPUS Cited Count: 2

30 Days PV: 1

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