Author Affiliations
1 School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China2 School of Information Science and Technology, Donghua University, Shanghai 201620, Chinashow less
Fig. 1. Radar scatter plot
Fig. 2. Hierarchies of the ground truth. (a) 12 hierarchies; (b) 22 hierarchies; (c) 32 hierarchies; (d) original ground truth
Fig. 3. Architecture of proposed network
Fig. 4. Comparison of (a) 2D convolution and (b) 3D convolution
Fig. 5. Data acquisition equipment
Fig. 6. Infrared imaging and radar scatter plot at corresponding time. (a) Infrared imaging; (b) radar scatter points
Fig. 7. Comparison of traditional methods. (a) Scenario1; (b) scenario2; (c) scenario3; (d) scenario4
Fig. 8. Comparison of proposed method with traditional methods
Fig. 9. Data joint distribution. (a) Scenario1; (b) scenario2; (c) scenario3; (d) scenario4
Fig. 10. Comparison of experimental results. (a) Scenario1; (b) scenario2; (c) scenario3; (d) scenario4
Layer | Conv1kernel stride | Conv2kernel stride |
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Init | 1×1 | 1×1 | Residual block1 init | 1×1 | 1×1 | Residual block2 init | 2×2 | 1×1 | Residual block2 init | 2×2 | 1×1 |
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Table 1. Convolutional kernel stride of 2D network residual blocks
Layer | Channel | Kernel | Stride | Layer | Channel | Kernel | Stride |
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Conv1 | 64 | 1×1×1 | 1×1×1 | Conv4a | 512 | 1×1×1 | 1×1×1 | Pool1 | 64 | 1×2×2 | 1×2×2 | Conv4b | 512 | 1×1×1 | 1×1×1 | Conv2 | 128 | 1×1×1 | 1×1×1 | Pool4 | 512 | 2×2×2 | 2×2×2 | Pool2 | 128 | 2×2×2 | 2×2×2 | Conv5a | 512 | 1×1×1 | 1×1×1 | Conv3a | 256 | 1×1×1 | 1×1×1 | Conv5b | 512 | 1×1×1 | 1×1×1 | Conv3b | 256 | 1×1×1 | 1×1×1 | Pool5 | 512 | 2×2×2 | 2×2×2 | Pool3 | 256 | 2×2×2 | 2×2×2 | Fc | - | - | |
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Table 2. Architecture of 3D network
Method | δ<1.25 | δ<1.252 | δ<1.253 | REL | log 10 | RMSE |
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Proposed method | 0.769 | 0.899 | 0.938 | 0.196 | 0.081 | 3.112 | method | 0.752 | 0.890 | 0.925 | 0.218 | 0.087 | 3.201 | ResNet-32 | 0.741 | 0.875 | 0.919 | 0.241 | 0.087 | 3.285 | FCN-Vgg | 0.737 | 0.879 | 0.913 | 0.251 | 0.080 | 3.290 | method | 0.625 | 0.826 | 0.892 | 0.297 | 0.115 | 4.098 | FCN-AlexNet | 0.575 | 0.806 | 0.890 | 0.320 | 0.121 | 4.101 | MLP | 0.175 | 0.377 | 0.601 | 6.698 | 0.294 | 9.883 | SVM | 0.182 | 0.379 | 0.622 | 6.808 | 0.298 | 9.615 | KNN | 0.182 | 0.656 | 0.505 | 5.420 | 0.385 | 10.250 | DT | 0.257 | 0.508 | 0.698 | 6.242 | 0.276 | 10.323 |
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Table 3. Performance evaluation of comparative experiments