Article(id=1309639323415310890, tenantId=1146029695717560320, journalId=1146119893612605453, issueId=1309639316763149213, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1734883200000, receivedDateStr=2024-12-23, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1790171946623, onlineDateStr=2026-09-23, pubDate=1766160000000, pubDateStr=2025-12-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790171946623, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790171946623, creator=13701087609, updateTime=1790171946623, updator=13701087609, issue=Issue{id=1309639316763149213, tenantId=1146029695717560320, journalId=1146119893612605453, year='2025', volume='43', issue='12', pageStart='1563', pageEnd='1704', issueExtLink='null', onlineDate='null', pubDate='1766160000000', pubDateStr='2025-12-20', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790171945038, creator='13701087609', updateTime=1790231727008, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309890060590346990, tenantId=1146029695717560320, journalId=1146119893612605453, issueId=1309639316763149213, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309890060594541295, tenantId=1146029695717560320, journalId=1146119893612605453, issueId=1309639316763149213, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1638, endPage=1646, ext={EN=ArticleExt(id=1309639323570500139, articleId=1309639323415310890, tenantId=1146029695717560320, journalId=1146119893612605453, language=EN, title=An object detection algorithm for small-scale windmills, columnId=null, journalTitle=Renewable Energy Resources, columnName=null, runingTitle=null, highlight=null, articleAbstract=

Windmills are important facilities that convert wind energy into electrical energy. This paper addresses the issues of missed detections and false detections in the process of identifying small-scale windmills in remote sensing images by constructing a new GF-2 windmill dataset. It proposes the YOLO-WM algorithm for the small target detection task of windmills, adding a tiny object detection layer based on the YOLOv8 object detection algorithm, and incorporates a BSAM dynamic attention mechanism module in the backbone section. It replaces the C2f module with the lightweight C2f_RepGhost module and finally utilizes the SAHI slicing algorithm in the prediction stage to complete the windmill target detection task. Experimental results show that the YOLO-WM algorithm can better retain shallow convolutional features while reducing algorithm complexity, allowing for more accurate capture of small-scale target information. This method compensates for the shortcomings of small object detection datasets, and it has been verified that the improved algorithm performs better in detecting small-scale windmills than other algorithms. This research provides valuable insights for the rational optimization and allocation of wind energy resources and the joint analysis of geographical elements.

, authors=Yilin Bao1, Jinshan Hu1, Jianrong Kang1, Yihu Zhu2, Shengli Wang2, authorsList=Yilin Bao, Jinshan Hu, Jianrong Kang, Yihu Zhu, Shengli Wang, authorCompany=null, correspAuthors=Jinshan Hu, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1309639323771826732, articleId=1309639323415310890, tenantId=1146029695717560320, journalId=1146119893612605453, language=CN, title=一种针对小尺度风车的目标检测算法, columnId=null, journalTitle=可再生能源, columnName=, runingTitle=null, highlight=null, articleAbstract=

风车是将风能转换为电能的重要设施,针对遥感影像中小尺度风车识别过程中出现的漏检与误检问题,文章构建了新的高分二号风车数据集,提出了针对风车小目标检测任务的YOLO-WM算法。该算法在YOLOv8目标检测算法的基础上增加一个极小目标检测层,并在Backbone部分添加了BSAM动态注意力机制模块,使用轻量化C2f_RepGhost模块替换C2f模块,最后在预测阶段结合SAHI切片算法完成风车目标检测任务。实验结果表明,YOLO-WM算法在降低算法复杂度的同时更好地保留了浅层卷积特征,能更准确地捕捉小尺度目标信息。该方法弥补了小目标检测数据集的不足,经验证,改进算法在小尺度风车上的效果优于其他算法。文章研究结果对于风能资源合理优化配置与地理要素联合分析具有一定参考价值。

, authors=鲍熠琳1, 胡晋山1, 康建荣1, 朱益虎2, 王胜利2, authorsList=鲍熠琳, 胡晋山, 康建荣, 朱益虎, 王胜利, authorCompany=null, correspAuthors=胡晋山, authorNote=null, correspAuthorsNote=
胡晋山(1973-),男,博士,教授,硕士生导师,研究方向为时态GIS理论与应用。E-mail:
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一种针对小尺度风车的目标检测算法
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鲍熠琳 1 , 胡晋山 1 , 康建荣 1 , 朱益虎 2 , 王胜利 2
可再生能源 | 2025,43(12): 1638-1646
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可再生能源 | 2025 , 43 (12) : 1638 -1646
一种针对小尺度风车的目标检测算法
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鲍熠琳1, 胡晋山1 , 康建荣1, 朱益虎2, 王胜利2
作者信息
  • 1.江苏师范大学 地理测绘与城乡规划学院,江苏 徐州 221116
  • 2.江苏省地质测绘大队,江苏 南京 211102
通讯作者:
胡晋山(1973-),男,博士,教授,硕士生导师,研究方向为时态GIS理论与应用。E-mail:
An object detection algorithm for small-scale windmills
Yilin Bao1, Jinshan Hu1 , Jianrong Kang1, Yihu Zhu2, Shengli Wang2
Affiliations
  • 1.School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, China
  • 2.Jiangsu Geologic Surveying and Mapping Institute, Nanjing 211102, China
出版时间: 2025-12-20
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风车是将风能转换为电能的重要设施,针对遥感影像中小尺度风车识别过程中出现的漏检与误检问题,文章构建了新的高分二号风车数据集,提出了针对风车小目标检测任务的YOLO-WM算法。该算法在YOLOv8目标检测算法的基础上增加一个极小目标检测层,并在Backbone部分添加了BSAM动态注意力机制模块,使用轻量化C2f_RepGhost模块替换C2f模块,最后在预测阶段结合SAHI切片算法完成风车目标检测任务。实验结果表明,YOLO-WM算法在降低算法复杂度的同时更好地保留了浅层卷积特征,能更准确地捕捉小尺度目标信息。该方法弥补了小目标检测数据集的不足,经验证,改进算法在小尺度风车上的效果优于其他算法。文章研究结果对于风能资源合理优化配置与地理要素联合分析具有一定参考价值。

风车  /  深度学习  /  小目标检测  /  YOLOv8  /  高分影像

Windmills are important facilities that convert wind energy into electrical energy. This paper addresses the issues of missed detections and false detections in the process of identifying small-scale windmills in remote sensing images by constructing a new GF-2 windmill dataset. It proposes the YOLO-WM algorithm for the small target detection task of windmills, adding a tiny object detection layer based on the YOLOv8 object detection algorithm, and incorporates a BSAM dynamic attention mechanism module in the backbone section. It replaces the C2f module with the lightweight C2f_RepGhost module and finally utilizes the SAHI slicing algorithm in the prediction stage to complete the windmill target detection task. Experimental results show that the YOLO-WM algorithm can better retain shallow convolutional features while reducing algorithm complexity, allowing for more accurate capture of small-scale target information. This method compensates for the shortcomings of small object detection datasets, and it has been verified that the improved algorithm performs better in detecting small-scale windmills than other algorithms. This research provides valuable insights for the rational optimization and allocation of wind energy resources and the joint analysis of geographical elements.

windmills  /  deep learning  /  small target detection  /  YOLOv8  /  GF images
鲍熠琳, 胡晋山, 康建荣, 朱益虎, 王胜利. 一种针对小尺度风车的目标检测算法. 可再生能源, 2025 , 43 (12) : 1638 -1646 .
Yilin Bao, Jinshan Hu, Jianrong Kang, Yihu Zhu, Shengli Wang. An object detection algorithm for small-scale windmills[J]. Renewable Energy Resources, 2025 , 43 (12) : 1638 -1646 .
  • 国家自然科学基金项目(52074133; 41671395)
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  • 接收时间:2024-12-23
  • 首发时间:2026-09-23
  • 出版时间:2025-12-20
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  • 收稿日期:2024-12-23
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国家自然科学基金项目(52074133; 41671395)
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    1.江苏师范大学 地理测绘与城乡规划学院,江苏 徐州 221116
    2.江苏省地质测绘大队,江苏 南京 211102

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胡晋山(1973-),男,博士,教授,硕士生导师,研究方向为时态GIS理论与应用。E-mail:
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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
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