[1] Zeng Y S, Morris J. Detection limits of optical gas imagers as a function of temperature differential and distance[J]. Journal of the Air & Waste Management Association, 69, 351-361(2019).
[2] Gu X J, Lin H Q, Ding D W et al. An infrared gas imaging and instance segmentation based gas leakage detection method[J]. Journal of East China University of Science and Technology, 49, 76-86(2023).
[3] Yan G, Zhang L, Yu L et al. Mid-infrared methane sensor system for natural gas leakage detection and its application[J]. Chinese Journal of Lasers, 49, 1810001(2022).
[4] Li J Y, Li L H, Zhao S et al. Application research of tunable diode laser absorption spectroscopy in petroleum industry[J]. Laser & Optoelectronics Progress, 59, 1300006(2022).
[5] Gålfalk M, Bastviken D. Remote sensing of methane and nitrous oxide fluxes from waste incineration[J]. Waste Management, 75, 319-326(2018).
[6] Dierks S, Kroll A. Quantification of methane gas leakages using remote sensing and sensor data fusion[C](2017).
[7] Farnebäck G, Bigun J, Gustavsson T.. Two-frame motion estimation based on polynomial expansion[M]. Image analysis, 2749, 363-370(2003).
[8] Jia Y, Chen W G. Extracting turbulence parameters of smoke via video analysis[J]. AIP Advances, 11, 085003(2021).
[9] Horn B K P, Schunck B G. Determining optical flow[J]. Artificial Intelligence, 17, 185-203(1981).
[10] Zuo Q, Qi Y. A novel spatial-temporal optical flow method for estimating the velocity fields of a fluid sequence[J]. The Visual Computer, 33, 293-302(2017).
[11] Teng J H, Zhang Y G, Ai Y et al. Research and verification of gas displacement calculation model based on optical flow method[J]. Optics & Optoelectronic Technology, 19, 9-14(2021).
[12] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 60, 84-90(2012).
[13] Dosovitskiy A, Fischer P, Ilg E et al. FlowNet: learning optical flow with convolutional networks[C], 2758-2766(2015).
[14] Ilg E, Mayer N, Saikia T et al. FlowNet 2.0: evolution of optical flow estimation with deep networks[C], 1647-1655(2017).
[15] Sun D Q, Yang X D, Liu M Y et al. PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume[C], 8934-8943(2018).
[16] Teed Z, Deng J, Ferrari V, Hebert M, Sminchisescu C et al. RAFT: recurrent all-pairs field transforms for optical flow[M]. Computer vision-ECCV 2018, 12347, 402-419(2020).
[17] Jiang S H, Campbell D, Lu Y et al. Learning to estimate hidden motions with global motion aggregation[C], 9752-9761(2021).
[18] Rangel J, Dueñas C, Schmoll R et al. On evaluating deep learning-based optical flow methods for gas velocity estimation with optical gas imaging cameras[J]. Proceedings of SPIE, 11787, 1178704(2021).
[19] Eckert M L, Um K, Thuerey N. ScalarFlow: a large-scale volumetric data set of real-world scalar transport flows for computer animation and machine learning[J]. ACM Transactions on Graphics, 38, 239(2019).
[20] McGrattan K, Hostikka S, McDermott R et al. Fire dynamics simulator users guide NIST special publication 1019[R](2013).
[21] Dong Y H, Gao H L, Zhou J E et al. Evaluation of gas release rate through holes in pipelines[J]. Journal of Loss Prevention in the Process Industries, 15, 423-428(2002).
[22] Tomczak L J. GPU ray marching of distance fields[D], 8(2012).
[23] Flanigan D F. Limits of passive remote detection of hazardous vapors by computer simulation[J]. Proceedings of SPIE, 2763, 117-127(1996).
[24] Li J K. Research on the theory and method of passive gas leak infrared imaging detection[D](2015).
[25] Zeng Y S, Morris J, Sanders A et al. Methods to determine response factors for infrared gas imagers used as quantitative measurement devices[J]. Journal of the Air & Waste Management Association, 67, 1180-1191(2017).
[26] Qi R B, He S K, Li X T et al. Simulation of TDLAS direct absorption based on HITRAN database[J]. Spectroscopy and Spectral Analysis, 35, 172(2015).
[27] Mayer N, Ilg E, Fischer P et al. What makes good synthetic training data for learning disparity and optical flow estimation?[J]. International Journal of Computer Vision, 126, 942-960(2018).
[28] Jonschkowski R, Stone A, Barron J T, Vedaldi A, Bischof H, Brox T et al. What matters in unsupervised optical flow[M]. Computer vision-ECCV 2020, 12347, 557-572(2020).
[29] Xiang X Z, Zhai M L, Zhang R F et al. Deep optical flow supervised learning with prior assumptions[J]. IEEE Access, 6, 43222-43232(2018).
[30] Kroeger T, Timofte R, Dai D X, Leibe B, Matas J, Sebe N et al. Fast optical flow using dense inverse search[M]. Computer vision-ECCV 2016, 9908, 471-488(2016).
