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Oil Leakage Image Recognition of Peak Regulation Power Equipment Based on Logical Semantic Discrimination
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Qian SONG1, Wei-wen QI2, Bing FANG2, Qiang FAN2, Hui-juan ZHENG3, Fei HU3
Water Resources and Power | 2023, 41(5) : 199 - 202
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Water Resources and Power | 2023, 41(5): 199-202
• ELECTRICAL ENGINEERING •
Oil Leakage Image Recognition of Peak Regulation Power Equipment Based on Logical Semantic Discrimination
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Qian SONG1, Wei-wen QI2, Bing FANG2, Qiang FAN2, Hui-juan ZHENG3, Fei HU3
Affiliations
  • 1.Water and Innovation Department, State Grid Corporation of China, Beijing 100031, China
  • 2.Shaoxing Power Supply Company of State Grid Zhejiang Electric Power Co., Ltd., Shaoxing 310000, China
  • 3.State Grid Electric Power Research Institute, Nanjing 211106, China
Published: 2023-05-25 doi: 10.20040/j.cnki.1000-7709.2023.20221377
Outline
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To address the challenges in oil leakage image recognition of peak regulation power equipment, a new method is proposed by introducing logical rule discrimination strategy into the task of image recognition. The technology of histogram equalization is adopted to improve original image. Then Mask RCNN network is introduced to obtain the preliminary location and contour information of storage device, ground and suspected oil area. Based on the above information, positional relationships between objects are judged, and logical expressions are adopted to determine the oil leakage area. Example analysis is conducted based on filed images of peak regulation power equipment. The results indicate that the proposed framework solves the problems in oil leakage area recognition, largely boosting model performance.

peak regulation power equipment  /  oil leakage image recognition  /  positional relationship recognition  /  logical semantic discrimination
Qian SONG, Wei-wen QI, Bing FANG, Qiang FAN, Hui-juan ZHENG, Fei HU. Oil Leakage Image Recognition of Peak Regulation Power Equipment Based on Logical Semantic Discrimination[J]. Water Resources and Power, 2023 , 41 (5) : 199 -202 . DOI: 10.20040/j.cnki.1000-7709.2023.20221377
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Article Info
doi: 10.20040/j.cnki.1000-7709.2023.20221377
  • Receive Date：2022-07-03
  • Online Date：2026-01-28
  • Published：2023-05-25
Article Data
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History
  • Received：2022-07-03
  • Revised：2022-10-19
Funding
Affiliations
    1.Water and Innovation Department, State Grid Corporation of China, Beijing 100031, China
    2.Shaoxing Power Supply Company of State Grid Zhejiang Electric Power Co., Ltd., Shaoxing 310000, China
    3.State Grid Electric Power Research Institute, Nanjing 211106, China
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https://castjournals.cast.org.cn/joweb/sdnykx/EN/10.20040/j.cnki.1000-7709.2023.20221377
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