Patrol image analysis framework and deep learning method for power grid
With the development of intelligent manufacturing and IoT,UAV has been widely utilized by power grid enterprise in patrolling transmission lines.At the same time,massive patrol image data need to be analyzed urgently.A U2U image analysis framework was designed from user data collection,automatic ann...
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2020-08-01
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| Series: | Dianxin kexue |
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| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2020056/ |
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| author | Yuanning LI Baifeng NING Zhaojie DONG |
| author_facet | Yuanning LI Baifeng NING Zhaojie DONG |
| author_sort | Yuanning LI |
| collection | DOAJ |
| description | With the development of intelligent manufacturing and IoT,UAV has been widely utilized by power grid enterprise in patrolling transmission lines.At the same time,massive patrol image data need to be analyzed urgently.A U2U image analysis framework was designed from user data collection,automatic annotation and to user feedback.Two deep learning methods,which were faster R-CNN and SSD,were explored and applied in U2U to detect five types of power components,including insulators,dampers,grading rings and shielding rings.A refining method based on K-means++ was proposed for the parameters of anchor box.Experimental results demonstrate that the proposed methods can effectively improve the adaptability and detection accuracy of deep learning method for multiple-scale power components and provide a useful reference for subsequent defect detection and deep application of UAV patrolling. |
| format | Article |
| id | doaj-art-6c220e55aedf43da9a18abd8cb670dcf |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2020-08-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-6c220e55aedf43da9a18abd8cb670dcf2025-01-15T03:27:29ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012020-08-013616717459811936Patrol image analysis framework and deep learning method for power gridYuanning LIBaifeng NINGZhaojie DONGWith the development of intelligent manufacturing and IoT,UAV has been widely utilized by power grid enterprise in patrolling transmission lines.At the same time,massive patrol image data need to be analyzed urgently.A U2U image analysis framework was designed from user data collection,automatic annotation and to user feedback.Two deep learning methods,which were faster R-CNN and SSD,were explored and applied in U2U to detect five types of power components,including insulators,dampers,grading rings and shielding rings.A refining method based on K-means++ was proposed for the parameters of anchor box.Experimental results demonstrate that the proposed methods can effectively improve the adaptability and detection accuracy of deep learning method for multiple-scale power components and provide a useful reference for subsequent defect detection and deep application of UAV patrolling.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2020056/intelligent power systemdeep learningmultiple-component detectionUAV |
| spellingShingle | Yuanning LI Baifeng NING Zhaojie DONG Patrol image analysis framework and deep learning method for power grid Dianxin kexue intelligent power system deep learning multiple-component detection UAV |
| title | Patrol image analysis framework and deep learning method for power grid |
| title_full | Patrol image analysis framework and deep learning method for power grid |
| title_fullStr | Patrol image analysis framework and deep learning method for power grid |
| title_full_unstemmed | Patrol image analysis framework and deep learning method for power grid |
| title_short | Patrol image analysis framework and deep learning method for power grid |
| title_sort | patrol image analysis framework and deep learning method for power grid |
| topic | intelligent power system deep learning multiple-component detection UAV |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2020056/ |
| work_keys_str_mv | AT yuanningli patrolimageanalysisframeworkanddeeplearningmethodforpowergrid AT baifengning patrolimageanalysisframeworkanddeeplearningmethodforpowergrid AT zhaojiedong patrolimageanalysisframeworkanddeeplearningmethodforpowergrid |