Laser & Optoelectronics Progress, Volume. 58, Issue 16, 1615005(2021)
Salient Detection of Multisource Image Illumination and Edge Fusion
Figures & Tables(13)
Fig. 4. Salient map comparison between different algorithms. (a) RGB images; (b) T images; (c) EGNet; (d) EGNet+;(e) CPDNet; (f) CPDNet+; (g) PoolNet; (h) PoolNet+; (i) DMRA; (j) A2dele; (k) proposed algorithm; (l) GT
Fig. 5. P-R curves of different algorithms at different datasets. (a) VT821; (b) VT1000
Fig. 6. Qualitative comparison of salient maps for ablation analysis. (a) RGB images; (b) T images; (c) Baseline; (d) +I; (e) +IA; (f) proposed algorithm; (g) -E; (h) GT
Table 1. RGB, T image illumination classification
View tableTable 1. RGB, T image illumination classification
Condition L1 L2 L3 L4 L5 L6 L7 L8 L9 U 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0.85 1 Q 0 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0.85
Table 2. RGB, T image light fusion ratio
View tableTable 2. RGB, T image light fusion ratio
Parameter L1 L2 L3 L4 L5 L6 L7 L8 L9 η 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1
Table 3. Detail information extraction of RGB、T image
View tableTable 3. Detail information extraction of RGB、T image
Parameter R1 R2 R3 R4 R5 R6 σ 2 3 4 5 6 7 K 2 4 5 7 8 10
Table 4. RGB、T Image fusion rules
View tableTable 4. RGB、T Image fusion rules
L L1 L2 L3 L4 L5 L6 L7 L8 L9 D R6 R6 R5 R4 R3 R2 R2 R1 R1
Table 5. Quantitative comparison of different algorithms
View tableTable 5. Quantitative comparison of different algorithms
Algorithm VT821 VT1000 Fmax Fave MAE Fmax Fave MAE EGNet 0.7717 0.7459 0.0512 0.8796 0.8637 0.0400 EGNet+ 0.6994 0.6475 0.116 0.7840 0.7453 0.0938 CPDNet 0.7971 0.7882 0.0425 0.8869 0.8621 0.0326 CPDNet+ 0.7915 0.7796 0.0471 0.8738 0.8570 0.0333 PoolNet 0.8192 0.8046 0.0485 0.8795 0.8651 0.0411 PoolNet+ 0.8177 0.8086 0.0411 0.8759 0.8656 0.0313 DMRA 0.8411 0.8270 0.0417 0.8613 0.8444 0.0381 A2dele 0.7513 0.7482 0.0621 0.8609 0.8575 0.0401 CGL 0.780 0.744 0.0849 0.727 CRA 0.747 0.739 0.1083 0.693 Proposed algorithm 0.8433 0.8291 0.0375 0.9095 0.8943 0.0268
Table 6. Test results in VT1000 low light data subset
View tableTable 6. Test results in VT1000 low light data subset
Algorithm Fmax Fave MAE EGNet 0.6602 0.5825 0.1100 EGNet+ 0.8393 0.8136 0.0616 EGNet++ 0.8225 0.8036 0.0538 CPDNet 0.7662 0.7092 0.1060 CPDNet+ 0.8071 0.7827 0.0721 CPDNet++ 0.8164 0.8038 0.0546 PoolNet 0.5803 0.5578 0.1140 PoolNet+ 0.7922 0.7683 0.0622 PoolNet++ 0.7532 0.7135 0.0940 Proposed algorithm 0.8743 0.8651 0.0386
Table 7. Quantitative comparison results of ablation analysis
View tableTable 7. Quantitative comparison results of ablation analysis
Algorithm VT821 VT1000 Fmax Fave MAE Fmax Fave MAE Baseline 0.8256 0.8003 0.0445 0.8396 0.8315 0.0361 +I 0.8326 0.8125 0.0429 0.8459 0.8361 0.0342 +IA 0.8493 0.8139 0.0417 0.8956 0.8827 0.0330 Proposed algorithm 0.8433 0.8291 0.0375 0.9095 0.8943 0.0268 -E 0.8366 0.8193 0.0411 0.9051 0.8819 0.0271
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Yuhao Ning, Yu Liu, Shaochu Wang. Salient Detection of Multisource Image Illumination and Edge Fusion[J]. Laser & Optoelectronics Progress, 2021, 58(16): 1615005
Paper Information
Category: Machine Vision
Received: Oct. 26, 2020
Accepted: Dec. 14, 2020
Published Online: Aug. 19, 2021
The Author Email: Shaochu Wang (wangshaochu@tju.edu.cn)
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