Advanced Search
WU Jiazhou, LIU Jun, SHI Jiawen, ZHANG Sheng. Research on image segmentation and color recognition method of laser weld[J]. LASER TECHNOLOGY, 2023, 47(5): 723-728. DOI: 10.7510/jgjs.issn.1001-3806.2023.05.022
Citation: WU Jiazhou, LIU Jun, SHI Jiawen, ZHANG Sheng. Research on image segmentation and color recognition method of laser weld[J]. LASER TECHNOLOGY, 2023, 47(5): 723-728. DOI: 10.7510/jgjs.issn.1001-3806.2023.05.022
shu

Research on image segmentation and color recognition method of laser weld

  • School of Information Engineering, Nanchang Hangkong University, Nanchang 330063, China

More Information
  • Received Date: August 09, 2022
  • Revised Date: October 23, 2022
  • Published Date: September 24, 2023
    Graphical Abstract
    • Abstract
  • In order to reduce the influence of weld shape and color diversity on segmentation accuracy in laser weld semantic segmentation, an image semantic segmentation method based on attention mechanism was used to extract weld. The image in the weld was converted from RGB(red, green, blue) to HSV(hue, saturation, value) color space, and the weld surface color was recognized in HSV. The effects of three kinds of welds on region segmentation and color recognition were analyzed. The results show that the average pixel accuracy of the weld segmentation region is about 91.2%, and the segmentation effect of the attention U-Net model with attention mechanism is better. The results of automatic identification of weld surface color meet production requirements, and have broad application prospects in industrial production.
    • FullText(HTML)
    • References (25)
  • [1]
    LI H. Analysis and control of laser weld crack[C]//2020 3rd International Conference on Electron Device and Mechanical Engineering (ICEDME). New York, USA: IEEE, 2020: 192-195.
    [2]
    LI X, XIE J, ZHOU Y. Effects of oxygen contamination in the argon shielding gas in laser welding of commercially pure titanium thin sheet[J]. Journal of Materials Science, 2005, 40(13): 3437-3443. DOI: 10.1007/s10853-005-0447-8
    [3]
    HARWIG D D, FOUNTAIN C, ITTIWATTANA W, et al. Oxygen equivalent effects on the mechanical properties of titanium welds[J]. Welding Journal Research Supplement, 2000, 79(11): 305s-316s.
    [4]
    樊立民, 耿乃涛, 杨柳, 等. 钛合金焊接过程防氧化保护技术进展[J]. 钢铁钒钛, 2021, 42(6): 43-50. https://www.cnki.com.cn/Article/CJFDTOTAL-GTFT202106005.htm

    FAN L M, GENG N T, YANG L, et al. Progress of anti-oxidation protection technology in titanium alloy welding process[J]. Iron Steel Vanadium Titanium, 2021, 42(6): 43-50(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-GTFT202106005.htm
    [5]
    GUPTA S S, KWON T H, KIM K D. Color based image processing techniques to detect oxide film during welding[C]//2020 International Conference on Electronics, Information, and Communication (ICEIC). New York, USA: IEEE, 2020: 1-4.
    [6]
    HUA S, LI B, SHU L, et al. Defect detection method using laser vision with model-based segmentation for laser brazing welds on car body surface[J]. Measurement, 2021, 178: 109370. DOI: 10.1016/j.measurement.2021.109370
    [7]
    齐继阳, 李金燕, 陆震云, 等. 改进的Otsu法在焊接图像分割中的应用[J]. 焊接学报, 2016, 37(10): 97-100. https://www.cnki.com.cn/Article/CJFDTOTAL-HJXB201610024.htm

    QI J Y, LI J Y, LU Zh Y, et al. Application of improved Otsu algorithm to welding image segmentation[J]. Transaction of the China Welding Institution, 2016, 37(10): 97-100(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-HJXB201610024.htm
    [8]
    MALARVEL M, SETHUMADHAVAN G, BHAGI P C R, et al. An improved version of Otsu's method for segmentation of weld defects on X-radiography images[J]. Optik, 2017, 142: 109-118. DOI: 10.1016/j.ijleo.2017.05.066
    [9]
    戴漩领, 茅云生, 周永清. 基于改进分水岭的焊缝分割算法[J]. 船舶工程, 2021, 43(s1): 428-433. https://www.cnki.com.cn/Article/CJFDTOTAL-CANB2021S1094.htm

    DAI X L, MAO Y Sh, ZHOU Y Q. Weld segmentation algorithm based on improved watershed[J]. Ship Engineer, 2021, 43(s1): 428-433(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-CANB2021S1094.htm
    [10]
    ANAND R S, KUMAR P. Flaw detection in radiographic weldment images using morphological watershed segmentation technique[J]. NDT & E International, 2009, 42(1): 2-8.
    [11]
    RADI D, ABO-ELSOUD M E A, KHALIFA F. Accurate segmentation of weld defects with horizontal shapes[J]. NDT & E International, 2022, 126: 102599.
    [12]
    XU H, YAN Z H, JI B W, et al. Defect detection in welding radiographic images based on semantic segmentation methods[J]. Measurement, 2022, 188: 110569. DOI: 10.1016/j.measurement.2021.110569
    [13]
    GOLODOV V A, MALOSTEVA A A. Approach to weld segmentation and defect classification in radiographic images of pipe welds[J]. NDT & E International, 2022, 127: 102597.
    [14]
    常颖, 常大俊. 改进型卷积神经网络焊点缺陷识别算法研究[J]. 激光技术, 2020, 44(6): 779-783. DOI: 10.7510/jgjs.issn.1001-3806.2020.06.023

    CHANG Y, CHANG D J. Research on solder joint defect recognition algorithm based on improved convolutional neural network[J]. Laser Technology, 2020, 44(6): 779-783. DOI: 10.7510/jgjs.issn.1001-3806.2020.06.023
    [15]
    RONNEBERGER O, FISCHER P, BROX T. U-net: Convolutional networks for biomedical image segmentation[C]//International Conference on Medical Image Computing and Computer-assisted Intervention. Munich, Germany: Springer, 2015: 234-241.
    [16]
    吴量, 付殿臣, 程超. 基于Unet的多注意力脑肿瘤图像分割算法[J]. 计算机技术与发展, 2021, 31(12): 85-91. https://www.cnki.com.cn/Article/CJFDTOTAL-WJFZ202112015.htm

    WU L, FU D Ch, CHENG Ch. Multi-attention brain tumor image segmentation algorithm based on Unet[J]. Computer Technology and Development, 2021, 31(12): 85-91(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-WJFZ202112015.htm
    [17]
    SCHLEMPER J, OKTAY O, SCHAAP M, et al. Attention gated networks: Learning to leverage salient regions in medical images[J]. Medical Image Analysis, 2019, 53: 197-207.
    [18]
    李代林, 万杰, 朱化凤, 等. 基于HSV颜色空间的水下物体识别技术研究[J]. 中国科学: 物理学力学天文学, 2015, 45(8): 71-75. https://www.cnki.com.cn/Article/CJFDTOTAL-JGXK201508010.htm

    LI D L, WAN J, ZHU H F, et al. Underwater image recognition technology research based on HSV color space[J]. Scientia Sinca Physica, Mechanica & Astronomica, 2015, 45(8): 71-75(in Chin-ese). https://www.cnki.com.cn/Article/CJFDTOTAL-JGXK201508010.htm
    [19]
    ZHONG H, WANG R. A visual-degradation-inspired model with HSV color-encoding for contour detection[J]. Journal of Neuroscience Methods, 2021, 369: 109423.
    [20]
    CHERNOV V, ALANDER J, BOCHKO V. Integer-based accurate conversion between RGB and HSV color spaces[J]. Computers & Electrical Engineering, 2015, 46: 328-337.
    [21]
    张强, 李嘉锋, 卓力. 基于卷积神经网络的监控场景下车辆颜色识别[J]. 测控技术, 2017, 36(10): 11-14. https://www.cnki.com.cn/Article/CJFDTOTAL-IKJS201710004.htm

    ZHANG Q, LI J F, ZHUO L. Vehicle color recognition using convolutional neural network for urban surveillance images[J]. Measurement and Control Technology, 2017, 36(10): 11-14(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-IKJS201710004.htm
    [22]
    周欣, 刘硕迪, 潘薇, 等. 自然交通场景中的车辆颜色识别[J]. 计算机科学, 2021, 48(s1): 15-20. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJA2021S1003.htm

    ZHOU X, LIU Sh D, PAN W, et al. Vehicle color recognition in natural traffic scene[J]. Computer Science, 2021, 48(s1): 15-20(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-JSJA2021S1003.htm
    [23]
    BARGSHADY G, ZHOU X, DEO R C, et al. The modeling of human facial pain intensity based on Temporal Convolutional Networks trained with video frames in HSV color space[J]. Applied Soft Computing, 2020, 97: 106805.
    [24]
    SHELHAMER E, LONG J, DARRELL T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(4): 640-651.
    [25]
    CHEN L Ch, PAPANDREOU G, KOKKINOS I, et al. DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(4): 834-848.
    • Related Articles

  • Related Articles

    [1]MENG Yufan, ZHANG Lijun, HE Changtao, XIAO Jing, YANG Ningjing, FENG Guoying, HAN Jinghua. Research on the characteristics and mechanism of laser cleaning aircraft skin based on image processing[J]. LASER TECHNOLOGY, 2024, 48(3): 303-311. DOI: 10.7510/jgjs.issn.1001-3806.2024.03.002
    [2]LIU Yifei, SU Ya, YAO Xiaotian, CUI Shengwei, YANG Lijun, ZHOU Congcong, HE Song. An optimization method of image processing for OCT non-invasive blood glucose detection[J]. LASER TECHNOLOGY, 2023, 47(2): 178-184. DOI: 10.7510/jgjs.issn.1001-3806.2023.02.004
    [3]LI Wenlong, GE Hailong, REN Yuan, CHENG Wei. Application of image processing technology in temperature measurement of laser pool[J]. LASER TECHNOLOGY, 2018, 42(5): 599-604. DOI: 10.7510/jgjs.issn.1001-3806.2018.05.004
    [4]ZHANG Hai-zhuang, YAO Mei, LEI Ping, LI Peng, ZENG Qing-ping. Research of image processing method of far-field laser spots[J]. LASER TECHNOLOGY, 2013, 37(4): 460-463. DOI: 10.7510/jgjs.issn.1001-3806.2013.04.010
    [5]SU Ping, NIU Yan-xiong, LI Da-qian, NIU Hai-sha, LI Yi-nan, ZHANG Chao. Image acquisition and processing of a laser warning system based on planar array CCD[J]. LASER TECHNOLOGY, 2013, 37(3): 394-399. DOI: 10.7510/jgjs.issn.1001-3806.2013.03.028
    [6]GU Guo-qing, WANG Kai-fu, YAN Xin-jiu. Electronic speckle interferometry image processing based on homomorphic filtering[J]. LASER TECHNOLOGY, 2010, 34(6): 750-752,797. DOI: 10.3969/j.issn.1001-3806.2010.06.009
    [7]ZHANG Yu-peng, WANG Kai-fu. Application of LabVIEW and MATLAB in ESPI image processing[J]. LASER TECHNOLOGY, 2009, 33(6): 582-585,589. DOI: 10.3969/j.issn.1001-3806.2009.06.007
    [8]TANG Min, WANG Hui-nan. Computer processing and visualization of confocal microscopy images[J]. LASER TECHNOLOGY, 2007, 31(5): 558-560.
    [9]KANG Xiao-ping, LÜ Bai-da. Far-field divergence angle of nonparaxial rotationally symmetric Laguerre-Gaussian beams[J]. LASER TECHNOLOGY, 2006, 30(2): 181-182,185.
    [10]Zhang Yixiao, Du Jinglei, Gao Fuhua, Yao Jun, Zeng Yangsu, Guo Yongkang. Chirp filtering in fractional Fourier domain and its application in digital image processing[J]. LASER TECHNOLOGY, 2003, 27(1): 78-80.

Catalog

    Get Citation
    PDF
    XML
    Article views (9) PDF downloads (5) Cited by()
    Turn off MathJax
    Article Contents
    Abstract
    References
    Related Articles

    Links：

    more+

    Website Copyright © Editorial Department of LASER TECHNOLOGY 蜀ICP备10038222号-1

    Address：No. 7, Section 4, Renmin South Road, Chengdu, Sichuan Province, China Post Code：610041

    Tel：028-68011091/68011046 Email: jgjs@vip.163.com

    Supported by: Beijing Renhe Information Technology Co., Ltd. 

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return