Improved Flame Detection Algorithm Based on Salient Target Detection

Ming Lu; Jingang Tan; Zhiyi Zhang; Ming Chen; Wei He

doi:10.3788/LOP202259.0410012

Journals >Laser & Optoelectronics Progress >Volume 59 >Issue 4 >Page 0410012 > Article

Laser & Optoelectronics Progress
Vol. 59, Issue 4, 0410012 (2022)

Improved Flame Detection Algorithm Based on Salient Target Detection

Ming Lu^1、2, Jingang Tan^1、2, Zhiyi Zhang¹, Ming Chen³, and Wei He^1、*

Author Affiliations

¹Key Laboratory of Wireless Sensor Networks and Communication, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 201800, China

²University of Chinese Academy of Sciences, Beijing 100864, China

³Wuxi Hi-Tech Nano SensoringNet R&D Center of Chinese Academy of Sciences, Wuxi, Jiangsu 214135, China

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DOI: 10.3788/LOP202259.0410012 Cite this Article Set citation alerts

Ming Lu, Jingang Tan, Zhiyi Zhang, Ming Chen, Wei He. Improved Flame Detection Algorithm Based on Salient Target Detection[J]. Laser & Optoelectronics Progress, 2022, 59(4): 0410012 Copy Citation Text

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Fig. 1. Flow chart of proposed algorithm

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Fig. 2. Improved fire detection model

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Fig. 3. Feature extraction network

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Fig. 4. Short connection branch network

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Fig. 5. Dilated convolution module

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Fig. 6. Attention mechanism network

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Fig. 7. Bi-FPN

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Fig. 8. Part of sample dataset. (a)-(c) Positive samples; (d)-(f) negative samples

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Fig. 9. Partial test results

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Parameter	ResNet-18	ResNet-34	ResNet-50
MAE	0.0101	0.0138	0.0104
F_β_fg	0.8812	0.8815	0.8943
F_β_bg	0.9010	0.8802	0.8734

Table 1. Performance analysis of feature extraction networks with different depth

Parameter	Proposed algorithm	YOLOv4	RetinaNet
Precision	0.890	0.900	0.802
Recall	0.880	0.880	0.773
F_β	0.884	0.889	0.788

Table 2. Performance comparison between proposed algorithm and YOLOv4, RetinaNet

Parameter	Proposed algorithm	BASNet	PICANet
F_β	0.881	0.877	0.861
MAE	0.010	0.013	0.023

Table 3. Performance comparison between proposed algorithm and BASNet, PICANet

Parameter	Proposed algorithm	YOLOv4	RetinaNet	BASNet	PICANet
Detection speed /（frame⋅s^-1）	4	4	4.5	2	3
Model size /MB	121.5	256	145.7	348.5	188.9

Table 4. Comparison of reasoning speed and model size of different algorithms

Parameter	Proposed algorithm	YOLOv4
$N_{F P}$	0.020	0.200
$N_{F N}$	0.003	0.067
$N_{T P}$	0.980	0.800
$N_{T N}$	0.997	0.933

Table 5. Comparison of detection results between proposed algorithm and YOLOv4

Single ResNet	Double	Bi-FPN	Dilated Conv	Attention mechanism	MAE	F_β
	√				0.085	0.8079
	√	√			0.043	0.8521
	√	√	√		0.032	0.8658
	√	√	√	√	0.010	0.8840
√		√	√	√	0.032	0.8498

Table 6. Ablation experiments on flame dataset

Ming Lu, Jingang Tan, Zhiyi Zhang, Ming Chen, Wei He. Improved Flame Detection Algorithm Based on Salient Target Detection[J]. Laser & Optoelectronics Progress, 2022, 59(4): 0410012

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