[1] Adegbove M A, Fung W K, Karnik A. Recent advances in pipeline monitoring and oil leakage detection technologies: principles and approaches[J]. Sensors, 2019, 19(11): 2548.
[2] ZHOU S J, LIU C, ZHAO Y E, et al. Leakage diagnosis of heating pipenetwork based on BP neural network[J]. Sustainable Energy, Grids and Networks, 2022, 32: 100869.
[3] SUN Z K, RAO M M, CAO Y L, et al. Water supply pipeline leakage intelligent detection algorithm based on small and unbalanced data [J]. Journal of Graphics, 2022, 43(5): 825-831.
[4] SHI G H, QI W X, CHEN P, et al. Negative pressure wave and wavelet analysis to locate the heating pipeline leakage[J]. Journal of Vibration and Impact, 2021, 40(14): 212-218.
[5] XUE T T, LIU Y L, CHEN Z, et al. Design of pipeline leakage detection based on distributed temperature sensing technology[J]. China Science Paper, 2023, 18(8): 867-874, 889.
[6] GAO L, CAO J G. Research on wavelet basis construction based on the characteristics of acoustic emission signals in gas pipe leakage [J]. Journal of Vibration and Impact, 2023, 42(10): 128-135.
[7] XU Z Y, XIAO Q. Outside inspection and quantitative evaluation of pipe defects based on pulsed remote field eddy currents[J]. Journal of Electronic Measurement and Instrumentation, 2019, 33(2): 80-87.
[8] LI J Z, YU H J, GUO X L, et al. Leak detection in pipe using controllable and low-pressure transient analysis method[J]. Journal of Basic Science and Engineering, 2022, 30(4): 873-882.
[9] Fahimipirehgalin M, Trunzer E, Odenweller M, et al. Automatic visual leakage detection and localization from pipelines in chemical process plants using machine vision techniques[J]. Engineering, 2021, 7(6): 758-776.
[10] ZHANG L Z, XU C H, CHEN G M. The detection of high-temperature pipe leakage by infrared thermography[C]//Proceedings of the 2nd CCPS China Process Safety Conference, 2014: 389-394.
[11] ZHANG Y B, REN R F, LIANG P, et al. Experimental study on flaw detection of buried heat pipeline based by infrared thermal[J]. Chinese Journal of Scientific Instrument, 2020, 41(6): 161-170.
[12] Yahia M, Gawai R, Ali T, et al. Non-destructive water leak detection using multitemporal infrared thermography[J]. IEEE Access, 2021, 9: 72556-72567.
[13] XIE J, ZHANG Y, HE Z, et al. Automated leakage detection method of pipeline networks under complicated backgrounds by combining infrared thermography and Faster R-CNN technique[J]. Process Safety and Environmental Protection, 2023, 174: 39-52.
[14] ZHOU R L, WEN Z P, SU H Z. Detect submerged piping in river embankment by passive infrared thermography[J]. Measurement, 2022, 202: 111873.
[15] ZHAI P, WANG P. Application of the adaptive wiener filter in infrared image denoising for molten steel [J]. Infrared Technology, 2021, 43(7): 665-669.
[16] GUO C L, ZHAO X Y, ZHENG H Y, et al. Infrared image denoising method based on improved non-local means filter[J]. Infrared Technology, 2018, 40(7): 638-641.
[17] ZHAO X H, LI M X, NIE T, et al. An innovative approach for removing stripe noise in infrared images[J]. Sensors, 2023, 23: 6786.
[18] ZHANG X, SANIIE J, BAKHTIARI S, et al. Unsupervised learning for detection of defects in pulsed infrared thermography of metals[C]// IEEE International Conference on Electro Information Technology (EIT), 2022: 330-334.
[19] ZHANG X, SANIIE J, BAKHTIARI S, et al. Compression of pulsed infrared thermography data with unsupervised learning for nondestructive evaluation of additively manufactured metals[J]. IEEE Access, 2022, 10: 9094-9107.
[20] WANG H, HOU Y, HE Y, et al. A physical-constrained decomposition method of infrared thermography: pseudo restored heat flux approach based on ensemble bayesian variance tensor fraction[J]. IEEE Transactions on Industrial Informatics, 2023, 20(3): 3413-3424.
[21] Kumar A, Tomar H, Mehla Kumar V, et al, Stationary wavelet transform based ECG signal denoising method[J]. ISA Transactions, 2021, 114: 251-262.
[22] Kumar S, Alam K, Chauhan A. Fractional derivative based nonlinear diffusion model for image denoising[J]. SeMA Journal, 2022, 79: 355-364.
[23] WANG Y L. Study of algorithm in image processing based on the bilateral filter[D]. Xi'an: XiDian University, 2010.
[24] Bochkovskiy A, WANG C Y, LIAO H Y M. Yolov4: Optimal speed and accuracy of object detection[C]//IEEE Conference Computer Vision and Pattern Recognition, 2020: 10934-10951.
[25] Ministry of Housing and Urban-Rural Development of the People’s Republic of China. Standard for design of building water supply and drainage[S]. Beijing: China Planning Press, 2019.
[26] LIU R C, LI Y F, WANG H D, et al. A noisy multi-objective optimization algorithm based on mean and Wiener filters[J]. Knowledge-Based Systems, 2021, 228: 107215.
[27] Verma, K, Singh K B, Thoke A. S. An enhancement in adaptive median filter for edge preservation[J]. Procedia Computer Science, 2015, 48: 29-36.
[28] WEI M Q, FENG Y D, WANG W M, et al. Interval gradient based joint bilateral filtering for image texture removal[J]. Computer Science, 2018, 45(3): 31-36.
[29] REN S, HE K, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149.
[30] LIU W, Anguelov D, Erhan D, et al. SSD: single shot multi-box detector[C]//Proceedings of the IEEE European Conference on Computer Vision, 2016: 21-37.
[31] Redmon J, Farhad A. Yolov3: an incremental improvement[C]// Computer Vision and Pattern Recognition, 2018: 1068-1076.
