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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, authorsList=宋倩, 祁炜雯, 方冰, 范强, 郑慧娟, 胡飞)}, authors=[Author(id=1223204292077670625, tenantId=1146029695717560320, journalId=1205116964453384197, articleId=1223204290827767886, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=qian-song@sgcc.com.cn, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1223204293474373876, tenantId=1146029695717560320, journalId=1205116964453384197, articleId=1223204290827767886, authorId=1223204292077670625, language=EN, stringName=Qian SONG, firstName=Qian, middleName=null, lastName=SONG, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=
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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:qian-song@sgcc.com.cn
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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:qian-song@sgcc.com.cn
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