Advanced Search
CHI Dazhao, XU Zhixian, LIU Haichun, LI Qingsheng, GUO Qiang, SU Weigang, JIA Tao. An ultrasonic image mosaic method based on improved SIFT algorithm[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2024, 45(10): 1-7. DOI: 10.12073/j.hjxb.20240630001
Citation: CHI Dazhao, XU Zhixian, LIU Haichun, LI Qingsheng, GUO Qiang, SU Weigang, JIA Tao. An ultrasonic image mosaic method based on improved SIFT algorithm[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2024, 45(10): 1-7. DOI: 10.12073/j.hjxb.20240630001

An ultrasonic image mosaic method based on improved SIFT algorithm

More Information
  • Received Date: June 29, 2024
  • Available Online: September 26, 2024
  • A comprehensive non-destructive testing of large structures usually needs a series of C-scans. In order to obtain a panoramic image of the structure under test, the method of sub-image mosaic is studied. According to the dynamic process of ultrasonic imaging and combined with digital image processing technology, an improved image mosaic method for ultrasonic C-scan detection is proposed based on the traditional scale invariant feature transform (SIFT) algorithm. Firstly, in view of the low success rate of ultrasound image registration using the traditional SIFT algorithm, the obtained matching feature points are screened through the vector difference of the starting positions of ultrasonic probe. Secondly, a dynamic programming method is used to find the best stitching path. Finally, a gradual in and out fusion is carried out along the best path for stitching to improve the visual effect of the fused area. Artificial defect contained block and welded piece are prepared and tested. The results of ultrasonic image mosaic show that the improved SIFT algorithm can effectively stitch multiple ultrasonic C-scan sub-images into panoramic images, and the proposed method has high accuracy of feature point matching and small image fusion distortion, which is better than the conventional SIFT image mosaic algorithm. In the mosaic image, the positions of targets match well, which can achieve overall non-destructive evaluation of structural processing quality.

  • [1]
    张晨昊, 陈兵, 刘恒, 等. 增材制造AlSi10Mg超声横波和纵波孔隙率检测对比分析[J]. 焊接学报, 2023, 44(10): 111 − 119. doi: 10.12073/j.hjxb.20230414001

    Zhang Chenhao, Chen Bing, Liu Heng, et al. Comparison of ultrasonic transverse and longitudinal wave porosity detection in additive manufacturing of AlSi10Mg[J]. Transactions of the China Welding Institution, 2023, 44(10): 111 − 119. doi: 10.12073/j.hjxb.20230414001
    [2]
    王斌, 李升, 朱富慧, 等. 基于超声无损检测的扩散连接界面缺陷尺寸评估[J]. 焊接学报, 2020, 41(8): 34 − 38. doi: 10.12073/j.hjxb.20200323004

    Wang Bin, Li Sheng, Zhu Fuhui, et al. Evaluation on interfacial defect size of diffusion bonding based on ultrasonic non-destructive testing[J]. Transactions of the China Welding Institution, 2020, 41(8): 34 − 38. doi: 10.12073/j.hjxb.20200323004
    [3]
    迟大钊, 郭涛, 张闰琦, 等. 声阻法胶接结构缺陷实时成像检测[J]. 焊接学报, 2022, 43(11): 107 − 111. doi: 10.12073/j.hjxb.20220702001

    Chi Dazhao, Guo Tao, Zhang Runqi, et al. Study on real-time imaging detection of bonding defects by acoustic impedance method[J]. Transactions of the China Welding Institution, 2022, 43(11): 107 − 111. doi: 10.12073/j.hjxb.20220702001
    [4]
    陈尧, 冒秋琴, 石文泽, 等. 基于相位相干性的厚壁焊缝TOFD成像检测研究[J]. 机械工程学报, 2019, 55(4): 25-32.

    Chen Yao, Mao Qiuqin, Shi Wenze, et al. Research on ultrasonic TOFD imaging inspection for heavy-walled weld based on phase coherence characteristics[J] Journal of Mechanical Engineering. 2019, 55(4): 25-32.
    [5]
    Bazulin E G. TOFD echo signal processing to achieve superresolution[J]. Russian Journal of Nondestructive Testing, 2021, 57(5): 352 − 360. doi: 10.1134/S1061830921050053
    [6]
    王锐, 刘志宏, 吴杰峰, 等. 316L对接焊缝相控阵超声检测工艺模拟与试验验证[J]. 焊接学报, 2019, 40(11): 33 − 38. doi: 10.12073/j.hjxb.2019400284

    Wang Rui, Liu Zhihong, Wu Jiefeng, et al. Simulation and experimental verification of phased array ultrasonic testing process in 316L butt weld[J]. Transactions of the China Welding Institution, 2019, 40(11): 33 − 38. doi: 10.12073/j.hjxb.2019400284
    [7]
    汪认, 赵鹏, 何建英, 等. 基于超声相控阵的耐候钢接头疲劳裂纹动态监测[J]. 焊接学报, 2022, 43(12): 100 − 104. doi: 10.12073/j.hjxb.20211117001

    Wang Ren, Zhao Peng, He Jianying, et al. Fatigue crack dynamic monitoring of weathering steel joint based on ultrasonic phased array[J]. Transactions of the China Welding Institution, 2022, 43(12): 100 − 104. doi: 10.12073/j.hjxb.20211117001
    [8]
    杨敬, 吴斌, 焦敬品, 等. 奥氏体不锈钢焊缝超声阵列检测方法[J]. 焊接学报, 2022, 43(2): 1 − 10. doi: 10.12073/j.hjxb.20210617001

    Yang Jing, Wu Bin, Jiao Jingpin, et al. Nondestructive testing of austenitic welds using method of ultrasonic array[J]. Transactions of the China Welding Institution, 2022, 43(2): 1 − 10. doi: 10.12073/j.hjxb.20210617001
    [9]
    Chen L L, Zhao Y Q, Kong S G. SFA-guided mosaic transformer for tracking small objects in snapshot spectral imaging[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2023, 204: 223 − 236. doi: 10.1016/j.isprsjprs.2023.09.015
    [10]
    Liu Y F, Zhao, X N, Song Z H, et al. Detection of apple mosaic based on hyperspectral imaging and three-dimensional gabor[J]. Computers and Electronics in Agriculture, 2024, 222: 109051 doi: 10.1016/j.compag.2024.109051
    [11]
    Zhang K, Yin C, Cheng Y H, et al. Rapid defect detection for spacecraft in infrared reconstructed images using image mosaic technique[C]//The 21st World Congress of the International Federation of Automatic Control (IFAC), July 12-17, 2020, Berlin. San Francisco: Curran and Associates, Inc. , 2020: 5695-5700.
    [12]
    Lowe D G. Distinctive image features from scale-invariant interest points[J]. International Journal Of Computer Vision, 2004, 60(2): 91 − 110. doi: 10.1023/B:VISI.0000029664.99615.94
    [13]
    Huang Q H, Deng Q F, Li L, et al. Scoliotic imaging with a novel double-sweep 2.5-dimensional extended field-of-view ultrasound[J]. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2019, 66(8): 1304 − 1315. doi: 10.1109/TUFFC.2019.2920422
    [14]
    Tsourounis D, Kastaniotis D, Theoharatos C, et al. SIFT-CNN: when convolutional neural networks meet dense SIFT descriptors for image and sequence classification[J]. Journal of Image, 2022, 8(10): 256.
    [15]
    Wang F B, Paul T b, Chen W, et al. Multi-image mosaic with SIFT and vision measurement for microscale structures processed by femtosecond laser[J]. Optics and Lasers in Engineering, 2018, 100: 124 − 130. doi: 10.1016/j.optlaseng.2017.08.004
  • Related Articles

    [1]JIN Yuhua, ZHANG Lin, ZHANG Liangliang, WANG Xijing. Fatigue crack growth behavior of 7050 aluminum alloy friction stir welded joint[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2020, 41(10): 11-16. DOI: 10.12073/j.hjxb.20200709002
    [2]YANG Shangqing, XU Lianyong, ZHAO Lei, HAN Yongdian, JING Hongyang. Study on high temperature low cycle fatigue behavior of a novel austenitic heat-resistant steel[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2020, 41(5): 14-18. DOI: 10.12073/j.hjxb.20190718003
    [3]HAN Yongdian, ZHANG Zhaofu, XU Lianyong, ZHAO Lei, JING Hongyang. Study on high temperature low cycle fatigue behavior of P92 steel weld metal[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2019, 40(3): 11-14. DOI: 10.12073/j.hjxb.2019400063
    [4]KONG Da, ZHANG Liang, YANG Fan. Fatigue life prediction of SnAgCu-X solder joints based on Anand model[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2017, 38(4): 17-21. DOI: 10.12073/j.hjxb.20170404
    [5]YIN Chengjiang, SONG Tianmin, LI Wanli. Effect of high-temperature welding on fatigue life of 2.25Cr1Mo steel joint[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2015, 36(4): 106-108.
    [6]ZHAO Dongsheng, WU Guoqiang, LIU Yujun, LIU Wen, JI Zhuoshang. Effect of welding residual stress on fatigue life of Invar steel welded joint[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2013, (4): 93-95,108.
    [7]SUN Chengzhi, CAO Guangjun. Fatigue life simulation of spot weld based on equivalent structure stresses[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2011, (1): 105-108.
    [8]ZHANG Liang, XUE Songbai, HAN Zongjie, LU Fangyan, YU Shenglin, LAI Zhongmin. Fatigue life prediction of SnAgCu soldered joints of FCBGA device[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2008, (7): 85-88.
    [9]DING Yanchuang, ZHAO Wenzhong. Stiffness coordination strategy for increasing fatigue life and its application in welded structure[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2007, (12): 31-34.
    [10]Li Zhen, Zheng Xiulin. Prediction of Fatigue Life for Peened Butt Welds of 16Mn Steel[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 1997, (3): 151-158.

Catalog

    Article views (270) PDF downloads (91) Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return