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Journals >
Laser & Optoelectronics Progress >
Volume 58 >
Issue 24 >
Page 2411003 > Article
Laser & Optoelectronics Progress
Vol. 58, Issue 24, 2411003 (2021)
Improved Flame Target Detection Algorithm Based on YOLOv3
Binbin Zhang and Zilai·mahemuti Pa
*
Author Affiliations
School of Electrical Engineering, Xinjiang University, Urumqi, Xinjiang 830047, China
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DOI:
10.3788/LOP202158.2411003
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Binbin Zhang, Zilai·mahemuti Pa. Improved Flame Target Detection Algorithm Based on YOLOv3[J]. Laser & Optoelectronics Progress, 2021, 58(24): 2411003
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Fig. 1.
Structure of the residual module
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Fig. 2.
Structure of the YOLOv3 algorithm
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Fig. 3.
Schematic diagram of anchor prediction of YOLOv3 algorithm
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Fig. 4.
Structure of the large-scale GCM
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Fig. 5.
Structure of the CSA module
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Fig. 6.
Network structure of our algorithm
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Fig. 7.
Performance comparison between our algorithm and the YOLOv3 algorithm. (a) mAP; (b) F1 score; (c) Loss
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Fig. 8.
Detection results of different algorithms. (a) Original YOLOv3 algorithm; (b) our algorithm
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Algorithm
Category
Precision /%
Recall /%
F1 score /%
mAP /%
FPS
YOLOv3
fire
91.72
69.76
79.00
85.35
26.9
light
82.17
75.71
79.00
Retinanet
[
18
]
fire
90.71
78.43
84.00
86.22
18.5
light
83.21
81.43
82.00
Centernet-HG
[
19
]
fire
93.57
77.95
85.00
88.19
15.3
light
88.46
82.14
85.00
Ours
fire
95.07
78.90
86.00
89.82
20.2
light
89.39
84.29
87.00
Table 1.
Detection results of different algorithms
Network
FPN
CSA
GCM
Loss
mAP /%
1
×
×
×
×
85.35
2
√
×
×
×
87.80
3
√
√
×
×
88.52
4
√
√
√
×
88.97
5
√
√
√
√
89.82
Table 2.
Ablation experiment results
Abstract
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Binbin Zhang, Zilai·mahemuti Pa. Improved Flame Target Detection Algorithm Based on YOLOv3[J]. Laser & Optoelectronics Progress, 2021, 58(24): 2411003
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Paper Information
Category: Imaging Systems
Received: Jan. 20, 2021
Accepted: Mar. 4, 2021
Published Online: Nov. 30, 2021
The Author Email: Pa Zilai·mahemuti (294625876@qq.com)
DOI:
10.3788/LOP202158.2411003
Recommended Topics
laser devices and laser physics
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