Article(id=1223204290827767886, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223204286050452333, articleNumber=null, orderNo=null, doi=10.20040/j.cnki.1000-7709.2023.20221377, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1656777600000, receivedDateStr=2022-07-03, revisedDate=1666108800000, revisedDateStr=2022-10-19, acceptedDate=null, acceptedDateStr=null, onlineDate=1769564228969, onlineDateStr=2026-01-28, pubDate=1684944000000, pubDateStr=2023-05-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769564228969, onlineIssueDateStr=2026-01-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769564228969, creator=13701087609, updateTime=1769564228969, updator=13701087609, issue=Issue{id=1223204286050452333, tenantId=1146029695717560320, journalId=1205116964453384197, year='2023', volume='41', issue='5', pageStart='1', pageEnd='220', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1769564227831, creator=13701087609, updateTime=1769567742010, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1223219026013323264, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223204286050452333, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1223219026013323265, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223204286050452333, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=199, endPage=202, ext={EN=ArticleExt(id=1223204291192672373, articleId=1223204290827767886, tenantId=1146029695717560320, journalId=1205116964453384197, language=EN, title=Oil Leakage Image Recognition of Peak Regulation Power Equipment Based on Logical Semantic Discrimination, columnId=1222940986234364233, journalTitle=Water Resources and Power, columnName=ELECTRICAL ENGINEERING, runingTitle=null, highlight=null, articleAbstract=

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.

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针对调峰电源设备漏油图像识别中的难点问题,将逻辑规则判别引入图像识别领域,提出一种全新的图像识别方法。基于直方图均衡化技术增强原始图像,利用Mask RCNN网络初步获取储油装置、地面、疑似油污区域的位置和轮廓信息。根据这些信息判断物体间位置关系,并结合逻辑表达式判断疑似区域是否属于漏油区域。基于现场拍摄的调峰电源设备图像进行实例分析,结果表明该方法能有效克服漏油区域识别中的难点问题,大幅提升了识别准确率。

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祁炜雯(1980-),女,高级工程师,研究方向为电力系统,E-mail:
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宋倩(1978-),女,高级工程师,研究方向为抽水蓄能运行管理,E-mail:

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宋倩(1978-),女,高级工程师,研究方向为抽水蓄能运行管理,E-mail:

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基于逻辑语义判别的调峰电源设备漏油图像识别
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宋倩 1 , 祁炜雯 2 , 方冰 2 , 范强 2 , 郑慧娟 3 , 胡飞 3
水电能源科学 | 电气工程 2023,41(5): 199-202
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水电能源科学 | 电气工程 2023, 41(5): 199-202
基于逻辑语义判别的调峰电源设备漏油图像识别
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宋倩1 , 祁炜雯2 , 方冰2, 范强2, 郑慧娟3, 胡飞3
作者信息
  • 1.国网水新部,北京 100031
  • 2.国网浙江省电力有限公司绍兴供电公司,浙江 绍兴 310000
  • 3.国网电力科学研究院有限公司,江苏 南京 211106
  • 宋倩(1978-),女,高级工程师,研究方向为抽水蓄能运行管理,E-mail:

通讯作者:

祁炜雯(1980-),女,高级工程师,研究方向为电力系统,E-mail:
Oil Leakage Image Recognition of Peak Regulation Power Equipment Based on Logical Semantic Discrimination
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
出版时间: 2023-05-25 doi: 10.20040/j.cnki.1000-7709.2023.20221377
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针对调峰电源设备漏油图像识别中的难点问题,将逻辑规则判别引入图像识别领域,提出一种全新的图像识别方法。基于直方图均衡化技术增强原始图像,利用Mask RCNN网络初步获取储油装置、地面、疑似油污区域的位置和轮廓信息。根据这些信息判断物体间位置关系,并结合逻辑表达式判断疑似区域是否属于漏油区域。基于现场拍摄的调峰电源设备图像进行实例分析,结果表明该方法能有效克服漏油区域识别中的难点问题,大幅提升了识别准确率。

调峰电源设备  /  漏油图像识别  /  物体间关系识别  /  逻辑语义判别

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
宋倩, 祁炜雯, 方冰, 范强, 郑慧娟, 胡飞. 基于逻辑语义判别的调峰电源设备漏油图像识别. 水电能源科学, 2023 , 41 (5) : 199 -202 . DOI: 10.20040/j.cnki.1000-7709.2023.20221377
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
  • 国家电网有限公司总部科技项目(5700-202140381A-0-0-00)
2023年第41卷第5期
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文章信息
doi: 10.20040/j.cnki.1000-7709.2023.20221377
  • 接收时间:2022-07-03
  • 首发时间:2026-01-28
  • 出版时间:2023-05-25
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  • 收稿日期:2022-07-03
  • 修回日期:2022-10-19
基金
国家电网有限公司总部科技项目(5700-202140381A-0-0-00)
作者信息
    1.国网水新部,北京 100031
    2.国网浙江省电力有限公司绍兴供电公司,浙江 绍兴 310000
    3.国网电力科学研究院有限公司,江苏 南京 211106

通讯作者:

祁炜雯(1980-),女,高级工程师,研究方向为电力系统,E-mail:
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2种不同金属材料的力学参数

Family
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Number of
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种数
Number of
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Percentage of
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Genus
种数
Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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