Flame Image Detection Method Based on Deep Learning with Maximal Relevance and Minimal Redundancy

Fig. 8. Convolutional layers output feature maps. Shallow convolution layer: (a) network 1, (c) network 2; deep convolution layer: (b) network 1, (d) network 2

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Fig. 9. Variation curve of flame detection performance with number of features

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Table 1. Influence of different feature selection algorithms on recognition
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Table 1. Influence of different feature selection algorithms on recognition
Method Number of features ACCR /% DR /% FAR /%
Method 1 117 95.00 90.0 0
Ours 16 98.25 96.5 0

Table 2. Influence of CNN serial fusion features on recognition
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Table 2. Influence of CNN serial fusion features on recognition
Method Number offeatures ACCR /% DR /% FAR /%
Network 1 32 93.50 92.0 5.0
Network 2 27 96.00 95.5 3.5
Ours 16 98.25 96.5 0

Table 3. Performance comparison of three detection algorithms
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Table 3. Performance comparison of three detection algorithms
Algorithm Number offeatures ACCR /% DR /% FAR /%
Algorithm 1 3 76.25 84.0 31.5
Algorithm 2 16 98.25 96.5 0
Ours 19 99.75 100.0 0.5

Table 4. Results comparison of different detection algorithms
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Table 4. Results comparison of different detection algorithms
Algorithm ACCR /% DR /% FAR /%
Ref. [7] 86.88 91.85 18.10
Ref. [9] 96.73 94.78 1.32
Ref. [14] 97.28 98.25 3.70
Ours 99.75 100.00 0.50

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Zirui Li, Huiqin Wang, Yan Hu, Ying Lu. Flame Image Detection Method Based on Deep Learning with Maximal Relevance and Minimal Redundancy[J]. Laser & Optoelectronics Progress, 2020, 57(10): 101015

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