风力涡轮机叶片图像缺陷自动检测  

Automatic Detection of Image Defects in Wind Turbine Blades

作　　者：单立国 陈晨 刘书生 李金林 任大智 SHAN Liguo;CHEN Chen;LIU Shusheng;LI Jinlin;REN Dazhi(National Energy(Shandong)New Energy Co.,Ltd.,Weifang 261000,Shandong,China)

机构地区：[1]国家能源(山东)新能源有限公司,山东潍坊261000

出　　处：《电气传动自动化》2024年第3期33-36,共4页Electric Drive Automation

摘　　要：研究了风力涡轮机叶片图像的数据注释过程,以减少缺陷检测的不准确度,并在最近的深度学习架构上对基于补丁的检测框架的性能进行基准测试。在研究中确定了检测任务中的最大困难是由注释中存在极端的边界框纵横比而引起的,对两个附加注释集的实验表明,具有改变长方体纵横比的集能够提高整体缺陷检测精度,特别是对于包含具有非常小纵横比的长方体的类,并通过突出问题的可视化示例提供了大量的类结果。We studied the data annotation process of wind turbine blade images to reduce inaccuracies in defect detection,and benchmark the performance of patch based detection frameworks on the latest deep learning architecture.In the study,the biggest difficulty in the detection task was identified,which was caused by the extreme aspect ratio of bounding boxes in annotations.Experiments on two additional annotation sets showed that sets with changed cuboid aspect ratios can improve overall defect detection accuracy,especially for classes containing cuboids with very small aspect ratios.A large number of class results were also provided through visual examples highlighting the problem.

关 键 词：风力涡轮机 叶片图像 数据注释 深度学习 

分 类 号：TP274[自动化与计算机技术—检测技术与自动化装置]