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]CHEN Xiaohan, XU Yuanyuan. Infrared Multi-Scale Target Detection Algorithm Based on RCR-YOLO[J]. Infrared Technology , 2025, 47(4): 459-467.
    [2]LIU Xin, ZHANG Bin. Electronic Zooming of Infrared Image Based on Lightweight Multi-scale Aggregation Network[J]. Infrared Technology , 2025, 47(4): 445-452.
    [3]YE Baicheng, ZHU Youpan, ZHOU Yongkang, DUAN Chenhao, ZHANG Yudong, TAO Zhigang, FU Zhiyu. Review of Lightweight Target Detection Algorithms[J]. Infrared Technology , 2025, 47(3): 289-298.
    [4]CHEN Yonglin, WANG Hengtao, ZHANG Shang. Lightweight Infrared Target Detection Algorithm Based on YOLO v7[J]. Infrared Technology , 2024, 46(12): 1380-1389.
    [5]SHAO Yanhua, HUANG Qimeng, MEI Yanying, ZHANG Xiaoqiang, CHU Hongyu, WU Yadong. Multi-scale Anchor Construction Method for Object Detection[J]. Infrared Technology , 2024, 46(2): 162-167.
    [6]ZHOU Jinjie, JI Li, ZHANG Qian, ZHANG Baohui, YUAN Xilin, LIU Yanqing, YUE Jiang. Multiscale Infrared Object Detection Network Based on YOLO-MIR Algorithm[J]. Infrared Technology , 2023, 45(5): 506-512.
    [7]CHEN Yanlin, WANG Zhishe, SHAO Wenyu, YANG Fan, SUN Jing. Multi-scale Transformer Fusion Method for Infrared and Visible Images[J]. Infrared Technology , 2023, 45(3): 266-275.
    [8]SUN Shixin, ZHENG Zhiyun. Genetic Algorithm for Infrared Multi-target Detection Based on Multi-scale NNLoG Feature[J]. Infrared Technology , 2019, 41(9): 837-842.
    [9]SHEN Xu, CHENG Xiaohui, WANG Xinzheng. Infrared Dim-small Object Detection Algorithm Based on Adaptive Scale Local Contrast Enhancement Combined with Visual Attention Mechanism[J]. Infrared Technology , 2019, 41(8): 764-771.
    [10]WANG Yu-xiang, HAN Zhen-duo, WANG Hong-min. Detection Algorithm for Dim Infrared Target Based on Multi-Difference Factor[J]. Infrared Technology , 2012, 34(6): 351-355. DOI: 10.3969/j.issn.1001-8891.2012.06.009

  • Cited by

    Periodical cited type(5)

    1. 李鹏. 基于红外测温技术的农村配网设备运行监测研究. 中国新技术新产品. 2025(03): 127-129 .
    2. 樊慧文. 深度学习在输变电设备故障状态检测中的应用研究. 电工技术. 2025(02): 95-97+101 .
    3. 刘传洋，吴一全. 基于红外图像的电力设备识别及发热故障诊断方法研究进展. 中国电机工程学报. 2025(06): 2171-2196 .
    4. 李冰，杜喜英，王玉莹，翟永杰. 基于改进YOLOv8n的变电设备红外图像实例分割算法. 电子测量技术. 2024(10): 151-159 .
    5. 佟忠正，孙旸子. 基于U-Net网络的电力设备巡检图像增强模型及其自动控制研究. 自动化与仪表. 2024(11): 79-82+91 .

    Other cited types(1)

Catalog

    Get Citation
    PDF
    XML
    Article views PDF downloads Cited by(6)
    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