| 融合CLAHE算法与YOLOv4-tiny模型的变压器放电碳痕检测 |
| |
| 引用本文： | 魏菊芳,刘力卿,唐庆华,贺春,李松原,方琼.融合CLAHE算法与YOLOv4-tiny模型的变压器放电碳痕检测[J].变压器,2022,59(2):6-12. |
| |
| 作者姓名： | 魏菊芳 刘力卿 唐庆华 贺春 李松原 方琼 |
| |
| 作者单位： | 国网天津市电力公司电力科学研究院,天津300384;天津市电力物联网企业重点实验室,天津300384;国网天津市电力公司,天津300384 |
| |
| 基金项目： | 国网天津市电力科技项目(KJ20-1-04)。 |
| |
| 摘 要： | 借助机器视觉和深度学习进行变压器内部放电碳痕的检测识别,采用CLAHE算法进行图像特征增强和YOLOv4-tiny模型进行目标检测,验证其平均精度值满足实际检测需求。
|
| 关 键 词： | 变压器 放电碳痕 CLAHE YOLO-tiny模型 |
Transformer Discharge Carbon Trace Detection Based on CLAHE Algorithm and YOLOv4-tiny Model |
| |
| WEI Ju-fang,LIU Li-qing,TANG Qing-hua,HE Chun,LI Song-yuan,FANG Qiong.Transformer Discharge Carbon Trace Detection Based on CLAHE Algorithm and YOLOv4-tiny Model[J].Transformer,2022,59(2):6-12. |
| |
| Authors: | WEI Ju-fang LIU Li-qing TANG Qing-hua HE Chun LI Song-yuan FANG Qiong |
| |
| Affiliation: | (State Grid Tianjin Electric Power Research Institute,Tianjin 300384,China;Tianjin Key Laboratory of Internet of Things in Electricity,Tianjin 300384,China;State Grid Tianjin Electric Power Company,Tianjin 300384,China) |
| |
| Abstract: | Machine vision and deep learning are used to detect and identify the internal discharge carbon marks of the transformer.CLAHE algorithm is used to enhance image features and Yolov4-tiny model is used to detect targets.The average accuracy is verified to meet the actual detection requirements. |
| |
| Keywords: | Transformer Discharge carbon mark CLAHE YOLO-tiny Model |
| 本文献已被 维普 万方数据 等数据库收录！ |
|
