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[1] 苏志威,黄子涵,邱发生,郭朝阳,殷晓芳,邬冠华.基于改进YOLOv8的航空铝合金焊缝缺陷检测方法[J/OL].航空动力学报:1-10[2024-06-05]. https://doi.org/10.13224/j.cnki.jasp.20230414. 10.13224/j.cnki.jasp.20230414. SU Z W, HUANG Z H, QIU F S, GUO C Y, YIN X F, WU G H.Weld defect detection of aviation aluminum alloy based on improved YOLOv8[J/OL]. Journal of Aerospace Power:1-10[2024-06-05]. https://doi.org/10.13224/j.cnki.jasp.20230414.
[2] 程松,杨洪刚,徐学谦,李敏,陈云霞.基于YOLOv5的改进轻量型X射线铝合金焊缝缺陷检测算法[J].中国激光,2022,49(21):2104005. 10.3788/CJL202249.2104005. CHENG S, YANG H G, XU X Q, LI M, CHEN Y X. Improved lightweight X-ray aluminum alloy weld defects detection algorithm based on YOLOv5[J]. Chinese Journal of Lasers, 2022, 49(21): 2104005.
[3] LIU M Y, CHEN Y P, XIE J M, HE L, ZHANG Y. LF-YOLO:a lighter and faster YOLO for weld defect detection of X-ray image[J]. IEEE Sensors Journal, 2023, 23(7): 7430-7439. DOI:10.1109/JSEN.2023.3247006.
[4] 缪寅宵,孙增玉,杨奕,郭力振.基于深度学习的X射线胶片数字化与缺陷检测算法[J].航空制造技术,2023,66(7):50-56, 72. 10.16080/j.issn1671-833x.2023.07.050. MIAO Y X, SUN Z Y, YANG Y, GUO L Z. Algorithm of X-ray film digitization and defect detection based on depth learning[J]. Aeronautical Manufacturing Technology, 2023, 66(7): 50-56, 72.
[5] 刘欢,刘骁佳,王宇斐,王宁,曹立俊.基于复合卷积层神经网络结构的焊缝缺陷分类技术[J].航空学报,2022,43(增刊1):726928. 10.7527/S1000-6893.2022.26928. LIU H, LIU X J, WANG Y F, WANG N,CAO L J. Weld defect classification technology based on compound convolution neural network structure[J]. Acta Aeronautica et Astronautica Sinica, 2022, 43(S1): 726928.
[6] RABE P, REISGEN U, SCHIEBAHN A. Non-destructive evaluation of the friction stir welding process, generalizing a deep neural defect detection network to identify internal weld defects across different aluminum alloys[J]. Welding in the World, 2023, 67(3): 549-560. DOI: 10.1007/s40194-022-01441-y.
[7] PENG P, FAN K, FAN X Q, ZHOU H P, GUO Z Y. Real-time defect detection scheme based on deep learning for laser welding system[J]. IEEE Sensors Journal, 2023, 23(15): 17301-17309. DOI: 10.1109/JSEN.2023.3277732.
[8] 王睿,胡云雷,刘卫朋,李海涛.基于边缘AI的焊缝X射线图像缺陷检测[J].焊接学报,2022,43(1):79-84,118. 10.12073/j.hjxb.20210516001. WANG R, HU Y L, LIU W P, LI H T. Defect detection of weld X-ray image based on edge AI[J]. Transactions of the China Welding Institution, 2022, 43(1): 79-84, 118.
[9] CUI J H, ZHANG B X, WANG X P, WU J T, LIU J J, LI Y, et al. Impact of annotation quality on model performance of welding defect detection using deep learning[J]. Welding in the World, 2024, 68(4): 855-865. DOI: 10.1007/s40194-024-01710-y.
[10] 张勇,王鹏,吕志刚,邸若海,李晓艳,李亮亮.基于密集连接和多尺度池化的X射线焊缝缺陷分割方法[J].液晶与显示, 2024,39(1):59-68. 10.37188/CJLCD.2023-0088. ZHANG Y, WANG P, LYU Z G, DI R H, LI X Y, LI L L. X-ray weld defect detection method based on dense connection and multi-scale pooling[J]. Chinese Journal of Liquid Crystals and Displays, 2024, 39(1): 59-68.
[11] 刘金海,赵真,付明芮,左逢源,王雷.基于主动小样本学习的管道焊缝缺陷检测方法[J].仪器仪表学报,2022,43(11):252-261. 10.19650/j.cnki.cjsi.J2209872. LIU J H, ZHAO Z, FU M R, ZUO F Y, WANG L. Active small sample learning based the pipe weld defect detection method[J]. Chinese Journal of Scientific Instrument, 2022, 43(11): 252-261.
[12] IWATA K, MATSUMOTO T, AOYAMA K, KAJIKAWA K, GOTO K, SUGIMOTO K. Development of high-accuracy defect detection algorithm for X-ray welding image inspection under strong noise,low contrast and few samples[J]. Journal of the Japan Society for Precision Engineering, 2021, 87(12): 1003-1007. DOI: 10.2493/jjspe.87.1003.
[13] MAO H L, REN J M, TANG Y, MAO H Y, CHEN Y, YI X X, et al. Detection of weld defects using ultrasonic-guided waves based on matching pursuit and density peak clustering[J]. Transactions of the Institute of Measurement and Control, 2023, 45(8): 1470-1483. DOI: 10.1177/01423312221140618.
[14] 李可,吴忠卿,吉勇,宿磊.改进U-Net芯片X线图像焊缝气泡缺陷检测方法[J].华中科技大学学报(自然科学版),2022, 50(6):104-110. 10.13245/j.hust.220613. LI K, WU Z Q, JI Y, SU L. Detection method of weld bubble defect in chip X-ray image based on improved U-Net network[J]. Journal of Huazhong University of Science and Technology (Nature Science Edition), 2022, 50(6): 104-110.
[15] JIANG Z Y, JIANG Z G. Advanced optical coherence tomography for real-time detection of defects in aluminum alloy laser welding[J]. Tehni ki Vjesnik, 2024, 31(2): 339-344. DOI:10.17559/TV-20231011001015.
[16] ORLANDO M, DE MADDIS M, RAZZA V, LUNETTO V. Non-destructive detection and analysis of weld defects in dissimilar pulsed GMAW and FSW joints of aluminium castings and plates through 3D X-ray computed tomography[J]. The International Journal of Advanced Manufacturing Technology, 2024, 132(5): 2957-2970. DOI: 10.1007/s00170-024-13576-x.
[17] 张晓光,高顶.射线检测焊接缺陷的提取和自动识别[M].北京:国防工业出版社,2004:156-160. ZHANG X G, GAO D. Extraction and automatic recognition of welding defects in radiographic testing[M]. Beijing: National Defense University Press, 2004: 156-160.
[18] DA SILVA R R, MERY D. State-of-the-art of weld seam inspection by radiographic testing: part I–Image processing[J]. Materials Evaluation, 2007, 65(6): 643-647.
[19] DA SILVA R R, MERY D. State-of-the-art of weld seam radiographic testing: part II–Pattern recogtion[J]. Materials Evaluation, 2007, 65(8): 833-838.
[20] 高炜欣,胡玉衡,穆向阳,武晓萌.埋弧焊X射线焊缝图像缺陷分割检测技术[J].仪器仪表学报,2011,32(6):1215-1224. 10.19650/j.cnki.cjsi.2011.06.003. GAO W X, HU Y H, MU X Y, WU X M. Real-time detection and segmentation of submerged-arc welding defects in X-ray radiography images[J]. Chinese Journal of Scientific Instrument, 2011, 32(6): 1215-1224.
[21] 高炜欣,胡玉衡,武晓朦.基于压缩传感技术的埋弧焊X射线焊缝图像缺陷检测[J].焊接学报,2015,36(11):85-88,117. GAO W X, HU Y H, WU X M. A new algorithm for detecting defects of sub-arc welding X-ray image based on compress sensor theory[J]. Transactions of the China Welding Institution, 2015, 36(11): 85-88, 117.
[22] ALGHALANDIS S M, ALAMDARI G N. Welding defect pattern recognition in radiographicimages of gas pipelines using adaptive featureextraction method and neural network classifier[C]. Amsterdam: 23rd World Gas Conference, 2006: 1-13.
[1]曾忠刚 温秀荷 索杏兰 孔军 姜敏 陈凯 赵庆兵 曾朗峻.塔河凝析油管道结蜡计算[J].油气储运,2012,31(8):622.[doi:10.6047/j.issn.1000-8241.2012.08.018]
Zeng Zhongang,Wen Xiuhe,Suo Xinglan,et al.Wax deposition calculation for Tahe Condensate Pipeline[J].Oil & Gas Storage and Transportation,2012,31(09):622.[doi:10.6047/j.issn.1000-8241.2012.08.018]
[2]夏文鹤 茹黎南 李明 曹谢东.基于卫星物联网技术的油气管道远程监控[J].油气储运,2012,31(12):898.[doi:10.6047/j.issn.1000-8241.2012.12.006]
Xia Wenhe,Ru Linan,Li Ming,et al. Remote monitoring on oil and gas pipelines with satellite Internet of Things network[J].Oil & Gas Storage and Transportation,2012,31(09):898.[doi:10.6047/j.issn.1000-8241.2012.12.006]
[3]董绍华 韩忠晨 杨毅 曹兴.物联网技术在管道完整性管理中的应用[J].油气储运,2012,31(12):906.[doi:10.6047/j.issn.1000-8241.2012.12.008]
Dong Shaohua,Han Zhongchen,Yang Yi,et al.The application of Internet of Things technology in the integrity management of pipeline[J].Oil & Gas Storage and Transportation,2012,31(09):906.[doi:10.6047/j.issn.1000-8241.2012.12.008]
[4]李睿,侯宇,刘淑聪,等.锚纹特征对管道外防腐环氧涂层附着力的影响[J].油气储运,2011,30(05):355.[doi:10.6047/j.issn.1000-8241.2011.05.010]
Li Rui,Hou Yu,Li Jingmiao,et al.Impact of pattern on the adhesion of pipe external epoxy coating[J].Oil & Gas Storage and Transportation,2011,30(09):355.[doi:10.6047/j.issn.1000-8241.2011.05.010]
[5]段纪淼,宫敬,张宇,等.多相混输管道蜡沉积研究进展[J].油气储运,2011,30(04):241.[doi:10.6047/j.issn.1000-8241.2011.04.001]
Duan Jimiao,Gong Jing,Zhang Yu,et al.Research progress of wax deposition in multiphase mixed transmission pipeline[J].Oil & Gas Storage and Transportation,2011,30(09):241.[doi:10.6047/j.issn.1000-8241.2011.04.001]
[6]宋晓琴,刘广文,王雯娟.管道油品泄漏原因及其对环境的影响[J].油气储运,2011,30(04):297.[doi:10.6047/j.issn.1000-8241.2011.04.016]
Song Xiaoqin,Liu Guangwen,Wang Wenjuan. The influence of oil pipeline leakage on environmental pollution [J].Oil & Gas Storage and Transportation,2011,30(09):297.[doi:10.6047/j.issn.1000-8241.2011.04.016]
[7]蔡亮学,何利民,吕宇玲,等.水平定向钻管道穿越孔底泥浆的力学特性[J].油气储运,2011,30(01):25.[doi:10.6047/j.issn.1000-8241.2011.01.007]
Cai Liangxue,He Limin,Lv Yuling,et al.Hole-bottom slurry mechanical properties of horizontal directional drilling in pipeline crossing project[J].Oil & Gas Storage and Transportation,2011,30(09):25.[doi:10.6047/j.issn.1000-8241.2011.01.007]
[8]樊三新,刘玉玲,楚威威.高寒地区管道低温水试压方法[J].油气储运,2011,30(01):73.[doi:10.6047/j.issn.1000-8241.2011.01.020]
Fan Sanxin,Liu Yuling,Chu Weiwei.Pipeline hydrotest method of cold water in frigid zone[J].Oil & Gas Storage and Transportation,2011,30(09):73.[doi:10.6047/j.issn.1000-8241.2011.01.020]
[9]杜明俊,张振庭,张朝阳,等.多相混输管道90°弯管冲蚀破坏应力分析[J].油气储运,2011,30(06):427.[doi:10.6047/j.issn.1000-8241.2011.06.006]
Du Mingjun,Chao Ling,Fu Xiaodong,et al.Analysis of erosion fracture stress of 90° elbow in multi-phase mixed transmission pipeline[J].Oil & Gas Storage and Transportation,2011,30(09):427.[doi:10.6047/j.issn.1000-8241.2011.06.006]
[10]高河东,祁志江,任金岭,等.川气东送管道野三河跨越方案比选与实施[J].油气储运,2011,30(06):445.[doi:10.6047/j.issn.1000-8241.2011.06.011]
Gao Hedong,Qi Zhijiang,Ren Jinling,et al.Programs comparison and construction of the Yesanhe River Overhead Crossing NG Project of Sichuan-to-East Gas Pipeline[J].Oil & Gas Storage and Transportation,2011,30(09):445.[doi:10.6047/j.issn.1000-8241.2011.06.011]
[11]贾韶辉,李亚平,高炜欣,等.基于X射线图像与稀疏描述的管道环焊缝缺陷自动识别法[J].油气储运,2024,43(09):1.
JIA Shaohui,LI Yaping,GAO Weixin,et al.Research on intelligent identification method for pipeline girth weld defects based on machine vision[J].Oil & Gas Storage and Transportation,2024,43(09):1.
贾韶辉,男,1981年生,高级工程师,2013年博士毕业于中国地质大学(北京)地球探测与信息工程专业,现主要从事油气管道数字化、智能化专业方向的研究工作。地址:河北省廊坊市广阳区金光道51号,065000。电话:0316-2075367。Email:jiash@pipechina.com.cn
基金项目:国家管网集团科学研究与技术开发项目“管道大数据分析与应用研究”,WZXGL202107;陕西省重点研发计划项目“小径管及长输管道X射线焊缝图像缺陷自动检测系统”,2024GX-YBXM-003。
· Received: 2023-09-05 · Revised: 2023-12-13 · Online: 2024-06-21