| Citation: |
WANG Xia, XU Shiwei, DONG Kangjun, JIN Weiqi. Infrared Gas Image Segmentation Method Based on Background Modeling and Density Clustering[J]. Infrared Technology , 2024, 46(12): 1355-1361.
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Infrared imaging is an effective method for detecting gas leaks, enabling dynamic and visual observation of leakage occurrences. However, background interference and the intangible nature of gases often result in infrared images with indistinct gas-plume contours and reduced contrast. This study introduces a segmentation algorithm based on background modeling and density clustering that harnesses the spatiotemporal distribution characteristics of infrared gas images to segment gas regions in low-contrast infrared imagery. The foreground image was extracted by analyzing the matching relationship between the current frame and a sequence of frames using a Gaussian mixture model. Subsequently, a density clustering algorithm was applied to cluster the foreground image with spatial size constraints to filter out low-density regions. Morphological operations were performed to identify the gas-dispersion area. The experimental results indicate that the proposed algorithm can detect and segment low-contrast gas leaks within a scene. It significantly reduces noise and dynamic background interference, addresses voids in the gas region, and demonstrates distinct advantages over other algorithms. This offers a valuable reference for research on the segmentation of infrared images for gas-leak detection.
| [1] |
LI H, LUO D, SUN Y, et al. Classification and identification of industrial gases based on electronic nose technology[J]. Sensors, 2019, 19(22): 5033. DOI: 10.3390/s19225033
|
| [2] |
ZHU Y, QIAN X, LIU Z, et al. Analysis and assessment of the Qingdao crude oil vapor explosion accident: lessons learnt[J]. Journal of Loss Prevention in the Process Industries, 2015, 33: 289-303. DOI: 10.1016/j.jlp.2015.01.004
|
| [3] |
Zipser L, Wächter F, Franke H. Acoustic gas sensors using airborne sound properties[J]. Sensors and Actuators B: Chemical, 2000, 68(1-3): 162-167. DOI: 10.1016/S0925-4005(00)00478-0
|
| [4] |
Sekhar P K, Brosha E L. Trace detection of 2, 4, 6-trinitrotoluene using electrochemical gas sensor[J]. IEEE Sensors Journal, 2014, 15(3): 1624-1629.
|
| [5] |
XUE F, Adedokun G, XIE D, et al. A low power four-channel metal oxide semiconductor gas sensor array with T-shaped structure[J]. Journal of Microelectromechanical Systems, 2022, 31(2): 275-282. DOI: 10.1109/JMEMS.2022.3142155
|
| [6] |
Meribout M, Khezzar L, Azzi A, et al. Leak detection systems in oil and gas fields: Present trends and future prospects[J]. Flow Measurement and Instrumentation, 2020, 75: 101772. DOI: 10.1016/j.flowmeasinst.2020.1017721.007
|
| [7] |
Meribout M. Gas leak-detection and measurement systems: Prospects and future trends[J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 1-13.
|
| [8] |
Usamentiaga R, Venegas P, Guerediaga J, et al. Infrared thermography for temperature measurement and non-destructive testing[J]. Sensors, 2014, 14(7): 12305-12348. DOI: 10.3390/s140712305
|
| [9] |
Safitri A, Gao X, Mannan M S. Dispersion modeling approach for quantification of methane emission rates from natural gas fugitive leaks detected by infrared imaging technique[J]. Journal of Loss Prevention in the Process Industries, 2011, 24(2): 138-145. DOI: 10.1016/j.jlp.2010.11.007
|
| [10] |
ZHOU K, WANG Y, LV T, et al. Explore spatio-temporal aggregation for insubstantial object detection: benchmark dataset and baseline [C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022: 3104-3115.
|
| [11] |
WANG J, Tchapmi L P, Ravikumar A P, et al. Machine vision for natural gas methane emissions detection using an infrared camera[J]. Applied Energy, 2020, 257: 113998. DOI: 10.1016/j.apenergy.2019.113998
|
| [12] |
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, 2020, 135: 106780.
|
| [13] |
Bin J, Bahrami Z, Rahman C A, et al. Foreground fusion-based liquefied natural gas leak detection framework from surveillance thermal imaging[J]. IEEE Transactions on Emerging Topics in Computational Intelligence, 2022, 7(4): 1151-1162.
|
| [14] |
Park J S, SONG J K. FCN based gas leakage segmentation and improvement using transfer learning[C]//2019 IEEE Student Conference on Electric Machines and Systems (SCEMS 2019)of IEEE, 2019: 1-4.
|
| [15] |
WANG Z, LIAO K, XIONG J, et al. Moving object detection based on temporal information[J]. IEEE Signal Processing Letters, 2014, 21(11): 1403-1407. DOI: 10.1109/LSP.2014.2338056
|
| [16] |
Agarwal A, Gupta S, Singh D K. Review of optical flow technique for moving object detection[C]//2016 2nd International Conference on Contemporary Computing and Informatics (IC3I)of IEEE, 2016: 409-413.
|
| [17] |
XUE W, JIANG T. An adaptive algorithm for target recognition using Gaussian mixture models[J]. Measurement, 2018, 124: 233-240. DOI: 10.1016/j.measurement.2018.04.019
|
| [18] |
Barnich O, Van Droogenbroeck M. ViBe: a powerful random technique to estimate the background in video sequences[C]//2009 IEEE International Conference on Acoustics, Speech and Signal Processing of IEEE, 2009: 945-948.
|
| [19] |
谈和平, 夏新林, 刘林华, 等. 红外辐射特性与传输的数值计算[M]. 哈尔滨: 哈尔滨工业大学出版社, 2006.
TAN Heping, XIA Xinlin, LIU Linhua, et al. Numerical Simulation for Infrared Radiation Characteristics and Transmission[M]. Harbin: Harbin Institute of Technology Press, 2006.
|
| [20] |
张旭, 金伟其, 李力, 等. 天然气泄漏被动式红外成像检测技术及系统性能评价研究进展[J]. 红外与激光工程, 2019, 48(S2): 53-65.
ZHANG Xu, JIN Weiqi, LI Li, et al. Research progress on passive infrared imaging detection technology and system performance evaluation of natural gas leakage[J]. Infrared and Laser Engineering, 2019, 48(S2): 53-65.
|
| [21] |
ZUO J, JIA Z, YANG J, et al. Moving target detection based on improved Gaussian mixture background subtraction in video images[J]. IEEE Access, 2019, 7: 152612-152623. DOI: 10.1109/ACCESS.2019.2946230
|
| [22] |
SHEN J, HAO X, LIANG Z, et al. Real-time superpixel segmentation by DBSCAN clustering algorithm[J]. IEEE Transactions on Image Processing, 2016, 25(12): 5933-5942. DOI: 10.1109/TIP.2016.2616302
|
| [23] |
Dhanachandra N, Manglem K, Chanu Y J. Image segmentation using K-means clustering algorithm and subtractive clustering algorithm[J]. Procedia Computer Science, 2015, 54: 764-771. DOI: 10.1016/j.procs.2015.06.090
|
| [24] |
Ankerst M, Breunig M M, Kriegel H P, et al. OPTICS: Ordering points to identify the clustering structure[J]. ACM Sigmod Record, 1999, 28(2): 49-60. DOI: 10.1145/304181.304187
|
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