Advanced Search
中文
Volume 45 Issue 5
May  2023
Turn off MathJax
Article Contents
Abstract
References
Related Articles
ZENG Shuiling, TANG Minzhi. Infrared Image Segmentation for Electrical Equipment based on Fuzzy Inference[J]. Infrared Technology , 2023, 45(5): 446-454.
Citation: ZENG Shuiling, TANG Minzhi. Infrared Image Segmentation for Electrical Equipment based on Fuzzy Inference[J]. Infrared Technology , 2023, 45(5): 446-454.
shu

Infrared Image Segmentation for Electrical Equipment based on Fuzzy Inference

  • 1.

    Jishou University, College of Information Science and Engineering, Jishou 416000, China

  • 2.

    Key Laboratory of Image and Video Understanding for Social Safety, Nanjing University of Science and Technology, Nanjing 210094, China

More Information
  • Received Date: March 29, 2022
  • Revised Date: May 10, 2022
    Graphical Abstract
    • Abstract
  • Fuzzy theory is considered to address the uncertainty of infrared image segmentation of electrical equipment, and a new algorithm based on fuzzy inference for infrared image segmentation of electrical equipment is proposed in this paper. First, the intensity, global fault probability, and local grayscale features were defined using the pixel grayscale of the fault region in the infrared image of the electrical equipment, Mahalanobis distance between pixel points, image center of mass, and image dilation operation. Subsequently, 27 fuzzy rules were formulated based on the fuzzy language values of the features, and an infrared image segmentation algorithm based on fuzzy inference was designed. Finally, the algorithm was compared with the traditional Otsu and FCM algorithms in terms of subjective and objective evaluation indexes. Further, the experimental results show that the segmentation accuracy and misclassification error of the proposed algorithm are better than those of the other two algorithms. The algorithm can filter out interference regions with high luminance in infrared images, and exhibits a better segmentation effect on infrared images with small luminance differences and small fault areas.
    • FullText(HTML)
    • References (14)
  • [1]
    苏海锋, 赵岩, 武泽君, 等. 基于改进RetinaNet的电力设备红外目标精细化检测模型[J]. 红外技术, 2021, 43(11): 1104-1111. http://hwjs.nvir.cn/article/id/3233a6a1-cbf0-4110-baa5-2a56e551f092

    SU Haifeng, ZHAO Yan, WU Zejun, et al. Refined infrared object detection model for power equipment based on improved RetinaNet[J]. Infrared Technology, 2021, 43(11): 1104-1111. http://hwjs.nvir.cn/article/id/3233a6a1-cbf0-4110-baa5-2a56e551f092
    [2]
    陈飞. 改进的交互式Otsu红外图像分割算法[J]. 计算机测量与控制, 2020, 28(9): 248- 251. https://www.cnki.com.cn/Article/CJFDTOTAL-JZCK202009049.htm

    CHEN Fei. An improved interactive Otsu infrared image segmentation algorithm[J]. Computer Measurement & Control, 2020, 28(9): 248-251. https://www.cnki.com.cn/Article/CJFDTOTAL-JZCK202009049.htm
    [3]
    YU X, ZHOU Z, GAO Q, et al. Infrared image segmentation using growing immune field and clone threshold[J]. Infrared Physics & Technology, 2018, 88: 184-193. http://www.sciencedirect.com/science/article/pii/S1350449517306308
    [4]
    冯振新, 周东国, 江翼, 等. 基于改进MSER算法的电力设备红外故障区域提取方法[J]. 电力系统保护与控制, 2019, 47(5): 123-128. https://www.cnki.com.cn/Article/CJFDTOTAL-JDQW201905015.htm

    FENG Zhenxin, ZHOU Dongguo, JIANG Yi, et al. Fault region extraction using improved MSER algorithm with application to the electrical system[J]. Power System Protection and Control, 2019, 47(5): 123-128. https://www.cnki.com.cn/Article/CJFDTOTAL-JDQW201905015.htm
    [5]
    HU F, CHEN H, WANG X. An intuitionistic Kernel-based fuzzy C-means clustering algorithm with local information for power equipment image segmentation[J]. IEEE Access, 2020, 8: 4500-4514. DOI: 10.1109/ACCESS.2019.2963444
    [6]
    QI C, LI Q, LIU Y, et al. Infrared image segmentation based on multi-information fused fuzzy clustering method for electrical equipment[J]. International Journal of Advanced Robotic Systems, 2020, 17(2): 1729881420909600. http://www.xueshufan.com/publication/3009600905
    [7]
    WEI D, WANG Z, SI L, et al. An image segmentation method based on a modified local-information weighted intuitionistic Fuzzy C-means clustering and gold-panning algorithm[J]. Engineering Applications of Artificial Intelligence, 2021, 101: 104209. DOI: 10.1016/j.engappai.2021.104209
    [8]
    BAI X, LIU M, WANG T, et al. Feature based fuzzy inference system for segmentation of low-contrast infrared ship images[J]. Applied Soft Computing, 2016, 46: 128-142. DOI: 10.1016/j.asoc.2016.05.004
    [9]
    王力斌, 刘树伟. 基于模糊推理的油门防误踩系统控制研究[J]. 控制工程, 2020, 27(8): 1462-1467. https://www.cnki.com.cn/Article/CJFDTOTAL-JZDF202008026.htm

    WANG Libin, LIU Shuwei. Research on the control system of preventing false stepping the accelerating pedal based on the fuzzy theory[J]. Control Engineering of China, 2020, 27(8): 1462-1467. https://www.cnki.com.cn/Article/CJFDTOTAL-JZDF202008026.htm
    [10]
    张洪群, 顾吟雪, 郭擎. 灰色关联分析与模糊推理边缘检测图像融合法[J]. 遥感信息, 2020, 35(1): 15-27. https://www.cnki.com.cn/Article/CJFDTOTAL-YGXX202001003.htm

    ZHANG Hongqun, GU Yinxue, GUO Qing. Image fusion based edge detection of grey relational analysis and fuzzy inference[J]. Remote Sensing Information, 2020, 35(1): 15-27. https://www.cnki.com.cn/Article/CJFDTOTAL-YGXX202001003.htm
    [11]
    Vijayakumar S, Santhi V. Speckle noise reduction in SAR images using fuzzy inference system[J]. International Journal of Fuzzy System Applications (IJFSA), 2019, 8(4): 60-83.
    [12]
    Arya K V. A new fuzzy rule based pixel organization scheme for optimal edge detection and impulse noise removal[J]. Multimedia Tools and Applications, 2020, 79(45): 33811-33837. DOI: 10.1007/s11042-020-08707-x?utm_source=xmol&utm_content=meta
    [13]
    BAI X, CHEN Z, ZHANG Y, et al. Infrared ship target segmentation based on spatial information improved FCM[J]. IEEE Transactions On Cybernetics, 2015, 46(12): 3259-3271. http://ieeexplore.ieee.org/document/7026038/
    [14]
    LONG J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015: 3431-3440.
    • Related Articles

  • Related Articles

    [1]FENG Hongwei, LIU Yuanyuan, WEN Ziteng, TAN Yong. Recognition Algorithm for an Infrared Flame Detector Based on an Improved Takagi-Sugeno Fuzzy Radial Basis Function Neural Network[J]. Infrared Technology , 2021, 43(1): 37-43.
    [2]HAO Zhenghui, ZHANG Xuesong, WANG Gao, DENG Fangfang, WEI Xuan, YUAN Dongfang. Auto Focusing Evaluation Function Based on Edge Contour Extraction[J]. Infrared Technology , 2018, 40(2): 170-175.
    [3]PAN Xuejuan, ZHU Youpan, PAN Chao, XIA Likun, ZENG Bangze, LUO Lin, ZHAO Deli, LI Zemin. The Influence of Energy Fluctuation of Image on Auto Focus Sharpness Evaluation Function[J]. Infrared Technology , 2016, 38(12): 1032-1037.
    [4]ZHAO Xiaoli, ZHOU Pucheng, XUE Mogen. A Kind of Infrared Image Segment Method Using Improved Chan-Vese Model[J]. Infrared Technology , 2016, 38(9): 774-778.
    [5]GUO Jingbin, FENG Huajie, WANG Long, PENG Qinjian, LI Xingfei. Design of Focusing Window Based on Energy Function of Gradient[J]. Infrared Technology , 2016, 38(3): 197-202.
    [6]YU Hao, LIU Bing-qi, YING Jia-ju, HU Wen-gang. One-Dimension Image Edge Detection Method Based on Sigmoidal Function Fitting[J]. Infrared Technology , 2014, (10): 816-819.
    [7]LIU En-fan, YANG Jiu-cheng, SHI Wen-jun, XU Guo-qiang. An Infrared Image Segmentation Approach based on Improved Chan-Vese Model[J]. Infrared Technology , 2011, 33(9): 545-551. DOI: 10.3969/j.issn.1001-8891.2011.09.013
    [8]WEI Tong-lei, ZENG Qing-ping, ZHOU Yan, BAI Bin. A Method for Radial Moving Small Targets Detecting[J]. Infrared Technology , 2007, 29(12): 712-715. DOI: 10.3969/j.issn.1001-8891.2007.12.008
    [9]CAO Zhan-hui, LI Yan-jun, ZHANG Ke, WU Pan-long. A Novel Linear Edge Extraction Method Based on Gaussian Function[J]. Infrared Technology , 2006, 28(4): 207-209. DOI: 10.3969/j.issn.1001-8891.2006.04.006
    [10]YU Rui-xing, LI Yan-jun, ZHANG Ke. Infrared Image Edge Detection Using Improved Bubble Function[J]. Infrared Technology , 2006, 28(1): 36-38. DOI: 10.3969/j.issn.1001-8891.2006.01.009

  • Cited by

    Periodical cited type(1)

    1. 肖沁，李正周，刘海毅. 基于场景自适应方向引导滤波的红外成像非均匀性校正方法. 光子学报. 2024(11): 253-265 .

    Other cited types(2)

Catalog

    Get Citation
    PDF
    XML
    Article views (176) PDF downloads (29) Cited by(3)
    Related

    Copyright © 《Infrared Technology》Editorial Office滇ICP备12000354号-3

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

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return