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Acta Optica Sinica, Volume. 39, Issue 8, 0810002(2019)

Substation Infrared Image Segmentation Based on Novel Threshold Selection Method

Qingsheng Zhao1、*, Yuying Wang1, Xuping Wang1, and Zun Guo2
Author Affiliations
  • 1 Shanxi Key Laboratory of Power System Operation and Control, Taiyuan University of Technology, Taiyuan, Shanxi 0 30024, China
  • 2 School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (8)
    Equations (0)
    References (20)
    Cited By (4)
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    Figures & Tables(8)
    Basic principle flow chart

    Fig. 1. Basic principle flow chart

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    Threshold selection flow chart

    Fig. 2. Threshold selection flow chart

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    Segmentation method. (a) Composite image; (b) our method; (c) traditional; (d) filter smoothing; (e) threshold selection process

    Fig. 3. Segmentation method. (a) Composite image; (b) our method; (c) traditional; (d) filter smoothing; (e) threshold selection process

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    Comparison of results of different segmentation methods. (a) Fuzzy entropy segmentation; (b) maximum type variance method; (c) Otsu; (d) K-means; (e) fuzzy threshold segmentation; (f) level set segmentation

    Fig. 4. Comparison of results of different segmentation methods. (a) Fuzzy entropy segmentation; (b) maximum type variance method; (c) Otsu; (d) K-means; (e) fuzzy threshold segmentation; (f) level set segmentation

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    Current transformer fault. (a) Infrared image; (b) our method segmentation; (c) fuzzy threshold segmentation; (d) K-means segmentation; (e) fuzzy entropy segmentation

    Fig. 5. Current transformer fault. (a) Infrared image; (b) our method segmentation; (c) fuzzy threshold segmentation; (d) K-means segmentation; (e) fuzzy entropy segmentation

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    Cable fault. (a) Infrared image; (b) our method segmentation; (c) fuzzy threshold segmentation; (d) K-means segmentation; (e) fuzzy entropy segmentation

    Fig. 6. Cable fault. (a) Infrared image; (b) our method segmentation; (c) fuzzy threshold segmentation; (d) K-means segmentation; (e) fuzzy entropy segmentation

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    Corrosion comparison. (a) Uncorroded current transformer; (b) traditional corrosion of current transformer; (c) our corrosion of current transformer; (d) uncorroded cable; (e) traditional corrosion of cable; (f) our corrosion of cable

    Fig. 7. Corrosion comparison. (a) Uncorroded current transformer; (b) traditional corrosion of current transformer; (c) our corrosion of current transformer; (d) uncorroded cable; (e) traditional corrosion of cable; (f) our corrosion of cable

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    • Table 1. Quantitative evaluation of segmentation effect

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      Table 1. Quantitative evaluation of segmentation effect

      ParameterOur methodLevel set segmentationFuzzy entropy segmentationMaximum type variance methodOtsuK-means segmentationFuzzy threshold segmentation
      IIOU0.8206050.2660980.5718780.7967950.6268340.6271340.664271
      EError0.0001820.0026950.0002990.000599000.000599
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    Qingsheng Zhao, Yuying Wang, Xuping Wang, Zun Guo. Substation Infrared Image Segmentation Based on Novel Threshold Selection Method[J]. Acta Optica Sinica, 2019, 39(8): 0810002

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    Paper Information

    Category: Image Processing

    Received: Jan. 15, 2019

    Accepted: Mar. 21, 2019

    Published Online: Aug. 7, 2019

    The Author Email: Qingsheng Zhao (zhaoqs1996@163.com)

    DOI:10.3788/AOS201939.0810002

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology