Xiaoyu Gu, Xiaojing Gu, Jie Ding, Xingsheng Gu. Estimation of Gas Velocity in Optical Gas Imaging Based on Deep Optical Flow Network[J]. Acta Optica Sinica, 2024, 44(18): 1812001
- Acta Optica Sinica
- Vol. 44, Issue 18, 1812001 (2024)
Fig. 1. Framework for generating gas optical flow dataset
Fig. 2. Schematic diagram of ray marching method
Fig. 3. Three-layer radiative transfer model
Fig. 4. Synthesized gas optical flow dataset. (a)(d)(g) Frame 1; (b)(e)(h) frame 2; (c)(f)(i) optical flow label
Fig. 5. Schematic diagram of gas column density and velocity estimation
Fig. 6. Estimation results of multiple optical flow methods on the gas optical flow validation set. (a) Frame 1; (b) frame 2; (c) optical flow label; (d) Farnebäck; (e) DIS; (f) FlowNet2; (g) PWC-Net; (h) RAFT; (i) GMA; (j) FlowNet2+ours
Fig. 7. Estimated optical flow results of real images. (a) Frame 1; (b) frame 2; (c) Farnebäck; (d) DIS; (e) FlowNet2; (f) PWC-Net; (g) RAFT; (h) GMA; (i) ours
Fig. 8. Continuous synthetic gas optical flow data. (a)(b)(c)(d) Frame 1, 20, 40, 60 gas images; (e)(f)(g)(h) frame 1, 20, 40, 60 gas optical flow label
Fig. 9. Comparison of warping transformations between the FlyingChairs and gas optical flow dataset. (a) Origin image in FlyingChairs; (b) origin image in gas optical flow dataset; (c) warped image in FlyingChairs; (d) warped image in gas optical flow dataset
Fig. 10. Comparisons of optical flow estimation results using different loss functions. (a)(b) Frame 1; (c)(d) frame 2; (e)(f) optical flow label; (g)(h) optical flow estimation results with gradient loss; (i)(j) optical flow estimation results without gradient loss
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Table 1. Performance comparison of different optical flow methods on the gas optical flow validation dataset
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Table 2. Comparison of velocity estimation accuracy of different optical flow methods on continuous synthetic gas data
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Table 3. AEPE obtained from different methods in ablation study
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