Optics and Precision Engineering, Volume. 31, Issue 20, 3034(2023)

Spatial information adaptive regulation and feature alignment for infrared methane instance segmentation

Zifen HE, Huizhu CAO, Yinhui ZHANG*, and Hong ZHUANG
Author Affiliations
  • Faculty of Mechatronics and Electrical Engineering, Kunming University of Science and Technology, Kunming650000, China
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    References(33)

    [1] [1] 熊仕富. 红外热成像甲烷气体探测与识别系统关键技术研究[D]. 长春: 长春理工大学, 2018.XIONGS F. Studies on Key Technologies of Infrared Thermal Imaging Detection and Identification System for Methane Gas[D]. Changchun: Changchun University of Science and Technology, 2018. (in Chinese)

    [2] KEYES T, RIDGE G, KLEIN M et al. An enhanced procedure for urban mobile methane leak detection[J]. Heliyon, 6(2020).

    [4] BOAZ L, KAIJAGE S, SINDE R. An Overview of Pipeline Leak Detection and Location Systems[C], 133-137(14).

    [5] JIN H, ZHANG L, LIANG W et al. Integrated leakage detection and localization model for gas pipelines based on the acoustic wave method[J]. Journal of Loss Prevention in the Process Industries, 27, 74-88(2014).

    [6] YANG C B, ROMANAK K D, REEDY R C et al. Soil gas dynamics monitoring at a CO2-EOR site for leakage detection[J]. Geomechanics and Geophysics for Geo-Energy and Geo-Resources, 3, 351-364(2017).

    [7] XU J H, NIE Z W, SHAN F Q et al. Leak detection methods overview and summary[C], 1034-1050(2012).

    [8] SCAFUTTO RD, DE SOUZA FILHO CR. Detection of heavy hydrocarbon plumes (Ethane, propane and Butane) using airborne longwave (7.6-13.5 μm) infrared hyperspectral data[J]. Fuel, 242, 863-870(2019).

    [9] HUANG K, MAO X. Detectability of infrared small targets[J]. Infrared Physics & Technology, 53, 208-217(2010).

    [10] [10] 丁德武, 申屠灵女, 邹兵, 等. 红外热成像技术在石化装置泄漏隐患检测中的应用[J]. 安全、健康和环境, 2015, 15(12): 17-20.DINGD W, SHENTUL N, ZOUB, et al. Application of infrared thermal imaging technology in detecting leakage of petrochemical plant[J]. Safety Health & Environment, 2015, 15(12): 17-20. (in Chinese)

    [11] [11] 鞠默然, 罗海波, 刘广琦, 等. 采用空间注意力机制的红外弱小目标检测网络[J]. 光学 精密工程, 2021, 29(4): 843-853. doi: 10.37188/OPE.20212904.0843JUM R, LUOH B, LIUG Q, et al. Infrared dim and small target detection network based on spatial attention mechanism[J]. Opt. Precision Eng., 2021, 29(4): 843-853. (in Chinese). doi: 10.37188/OPE.20212904.0843

    [13] XIONG K N, JIANG J B, PAN Y Y et al. Deep learning approach for detection of underground natural gas micro-leakage using infrared thermal images[J]. Sensors, 22, 5322(2022).

    [14] MELO R O, COSTA M G F, FILHO C F F C. Applying convolutional neural networks to detect natural gas leaks in wellhead images[J]. IEEE Access, 8, 191775-191784(2020).

    [15] SHI J, CHANG Y, XU C et al. Real-time leak detection using an infrared camera and Faster R-CNN technique[J]. Computers & Chemical Engineering, 135, 106780(2020).

    [16] HE K M, GKIOXARI G, DOLLÁR P et al. Mask R-CNN[C], 2980-2988(22).

    [17] HUANG Z J, HUANG L C, GONG Y C et al. Mask scoring R-CNN[C], 6402-6411(15).

    [18] CHEN K, PANG J M, WANG J Q et al. Hybrid task cascade for instance segmentation[C], 4969-4978(15).

    [19] LIN T Y, GOYAL P, GIRSHICK R et al. Focal loss for dense object detection[C], 2999-3007(22).

    [20] WANG X L, KONG T, SHEN C H et al. SOLO Segmenting Objects by Locations[M]. Computer Vision - ECCV 2020, 649-665(2020).

    [21] WANG X, ZHANG R, KONG T et al. Solov2: Dynamic and fast instance segmentation[J]. Advances in Neural information processing systems, 33, 17721-17732(2020).

    [22] TIAN Z, SHEN C H, CHEN H. Conditional Convolutions for Instance Segmentation[M]. Computer Vision - ECCV 2020, 282-298(2020).

    [23] BOLYA D, ZHOU C, XIAO F Y et al. YOLACT: Real-Time Instance Segmentation[C], 9156-9165(2019).

    [24] HE K M, ZHANG X Y, REN S Q et al. Deep residual learning for image recognition[C], 770-778(27).

    [25] TAN M X, PANG R M, LE Q V. EfficientDet: scalable and efficient object detection[C], 10778-10787(13).

    [26] SHELHAMER E, LONG J, DARRELL T. Fully convolutional networks for semantic segmentation[C], 640-651(2016).

    [27] [27] 郝帅, 何田, 马旭, 等. 动态特征优化机制下的跨尺度红外行人检测[J]. 光学 精密工程, 2022, 30(19): 2390-2403. doi: 10.37188/OPE.20223019.2390HAOS, HET, MAX, et al. Cross-scale infrared pedestrian detection based on dynamic feature optimization mechanism[J]. Opt. Precision Eng., 2022, 30(19): 2390-2403.(in Chinese). doi: 10.37188/OPE.20223019.2390

    [28] WANG K X, LIEW J H, ZOU Y T et al. PANet: Few-shot image semantic segmentation with prototype alignment[C], 9196-9205(2019).

    [29] FAN M Y, LAI S Q, HUANG J S et al. Rethinking BiSeNet for real-time semantic segmentation[C], 9711-9720(20).

    [30] CHENG T H, WANG X G, CHEN S Y et al. Sparse instance activation for real-time instance segmentation[C], 4423-4432(18).

    [31] ZHANG T, WEI S Q, JI S P. E2EC: an end-to-end contour-based method for high-quality high-speed instance segmentation[C], 4433-4442(18).

    [32] LIU H T, RIVERA SOTO R A, XIAO F Y et al. YolactEdge: real-time instance segmentation on the edge[C], 9579-9585(2021).

    [33] FANG Y X, YANG S S, WANG X G et al. Instances as queries[C], 6890-6899(10).

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    Zifen HE, Huizhu CAO, Yinhui ZHANG, Hong ZHUANG. Spatial information adaptive regulation and feature alignment for infrared methane instance segmentation[J]. Optics and Precision Engineering, 2023, 31(20): 3034

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    Paper Information

    Category: Information Sciences

    Received: Apr. 3, 2023

    Accepted: --

    Published Online: Nov. 28, 2023

    The Author Email: Yinhui ZHANG (yinhui_z@163.com)

    DOI:10.37188/OPE.20233120.3034

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