A light defect detection algorithm of power insulators from aerial images for power inspection
文献类型：期刊论文
| 作者 | Yang, Lei2 ; Fan, Junfeng1; Song, Shouan2; Liu, Yanhong2
|
| 刊名 | NEURAL COMPUTING & APPLICATIONS
 |
| 出版日期 | 2022-06-07 |
| 页码 | 11 |
| 关键词 | Insulator location Defect identification Transfer learning Dempster-Shafer evidence theory |
| ISSN号 | 0941-0643 |
| DOI | 10.1007/s00521-022-07437-5 |
| 通讯作者 | Liu, Yanhong(liuyh@zzu.edu.cn) |
| 英文摘要 | With the rapid growth of high-voltage transmission lines, the number of power transmission line equipments is correspondingly increasing. Power insulator is the basic component which plays the key role in the stable operation of power system. As a common defect of power insulators, missing-cap issue will affect the structural strength and durability of different power insulators. Therefore, the condition monitoring of power insulators is a daily but priority power line inspection task. Faced with the weak image features of small insulator defects in the aerial images, the conventional handcrafted features could not extract effectively powerful image features. Meanwhile, the small-scale insulator defects will bring a certain effect to the model training of deep learning. Therefore, the high-efficiency and accurate defect inspection still present a challenging task against complex backgrounds. To address the above issues, aimed at the missing-cap defects of power insulators, a novel defect identification algorithm from aerial images is proposed by taking advantage of state-of-the-art deep learning and transfer learning models. Fused with Spatial Pyramid Pooling (SPP) and MobileNet networks, a light deep convolutional neural network (DCNN) model based on You Only Look Once (YOLO) V3 network is proposed for fast and accurate insulator location to remove complex background interference. On the basis, combined with Dempster-Shafer (DS) evidence theory, the improved transfer learning model based on feature fusion is proposed for high-precision defect identification of power insulators. Experiments show that the proposed method could acquire a better identification performance against complex power inspection environment compared with other related detection models. |
| WOS关键词 | FAULT-DETECTION ; CLASSIFICATION |
| 资助项目 | National Key Research & Development Project of China[2020YFB1313701] ; National Natural Science Foundation of China[62003309] ; Science & Technology Research Project in Henan Province of China[202102210098] ; Outstanding Foreign Scientist Support Project in Henan Province of China[GZS2019008] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000806702300001 |
| 出版者 | SPRINGER LONDON LTD |
| 资助机构 | National Key Research & Development Project of China ; National Natural Science Foundation of China ; Science & Technology Research Project in Henan Province of China ; Outstanding Foreign Scientist Support Project in Henan Province of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/49515]  |
| 专题 | 复杂系统管理与控制国家重点实验室_水下机器人 |
| 通讯作者 | Liu, Yanhong |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Zhengzhou Univ, Sch Elect Engn, Zhengzhou 450001, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yang, Lei,Fan, Junfeng,Song, Shouan,et al. A light defect detection algorithm of power insulators from aerial images for power inspection[J]. NEURAL COMPUTING & APPLICATIONS,2022:11. |
| APA | Yang, Lei,Fan, Junfeng,Song, Shouan,&Liu, Yanhong.(2022).A light defect detection algorithm of power insulators from aerial images for power inspection.NEURAL COMPUTING & APPLICATIONS,11. |
| MLA | Yang, Lei,et al."A light defect detection algorithm of power insulators from aerial images for power inspection".NEURAL COMPUTING & APPLICATIONS (2022):11. |
入库方式： OAI收割
来源：自动化研究所
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