Vision-Based Power Line Segmentation With an Attention Fusion Network
文献类型：期刊论文
| 作者 | Yang, Lei2,3 ; Fan, Junfeng1; Xu, Shuai2,3; Li, En1; Liu, Yanhong2,3
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| 刊名 | IEEE SENSORS JOURNAL
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| 出版日期 | 2022-04-15 |
| 卷号 | 22期号:8页码:8196-8205 |
| 关键词 | Power transmission lines Inspection Feature extraction Sensors Image segmentation Navigation Vision sensors Power line extraction deep network architecture image segmentation multi-scale feature fusion attention module |
| ISSN号 | 1530-437X |
| DOI | 10.1109/JSEN.2022.3157336 |
| 通讯作者 | Xu, Shuai(xszzu2020@zzu.edu.cn) |
| 英文摘要 | Automatic power transmission line detection plays the key role in smart grid which has been widely applied into the path planning and navigation of intelligent inspection platforms, such as Unmanned Aerial Vehicles (UAVs), climbing robots, hybrid inspection robots, etc. Nevertheless, the power lines are always against complex background environment and different illumination conditions. And the power lines occupy a minimal portion image pixels in the aerial images compared with backgrounds which causes the foreground-background class imbalance issue. Therefore, robust and accurate vision-based power line detection still faces a certain challenge. Recently, deep learning has got fast development on pixel-level object segmentation due to strong contextual feature expression ability, especially U-shape network (U-Net) and its variants. However, it still exists a certain shortcomings owing to insufficient process of local contextual features to affect the segmentation precision. Meanwhile, multiple pooling operations in deep convolutional neural networks (DCNNs) also will cause the information loss. To address these issues, with the encoder-decoder architecture, a novel vision-based power line detection network is proposed in this paper to construct an end-to-end detection scheme of power lines from aerial images. To make the segmentation network capture the global contexts and emphasize target regions of power transmission lines, an attention block is proposed to be embedded into the proposed power line detection network to address the class imbalance issue. Meanwhile, faced with the insufficient process of local contextual feature maps of DCNNs, an attention fusion block is proposed for multi-scale feature fusion to acquire more rich information and improve the segmentation precision. Experiments on power lines show that the proposed power line detection network shows a good segmentation performance on real power line environment compared with other advanced detection methods. |
| WOS关键词 | INSPECTION ; DETECTOR |
| 资助项目 | National Natural Science Foundation of China[62003309] ; National Key Research and Development Project of China[2020YFB1313701] ; Science and 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 | Engineering ; Instruments & Instrumentation ; Physics |
| 语种 | 英语 |
| WOS记录号 | WOS:000803129500079 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Natural Science Foundation of China ; National Key Research and Development Project of China ; Science and 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/173211/49557]  |
| 专题 | 复杂系统管理与控制国家重点实验室_水下机器人 自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队 |
| 通讯作者 | Xu, Shuai |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Robot Percept & Control Engn Lab, Zhengzhou 450001, Henan, Peoples R China 3.Zhengzhou Univ, Sch Elect Engn, Zhengzhou 450001, Henan, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yang, Lei,Fan, Junfeng,Xu, Shuai,et al. Vision-Based Power Line Segmentation With an Attention Fusion Network[J]. IEEE SENSORS JOURNAL,2022,22(8):8196-8205. |
| APA | Yang, Lei,Fan, Junfeng,Xu, Shuai,Li, En,&Liu, Yanhong.(2022).Vision-Based Power Line Segmentation With an Attention Fusion Network.IEEE SENSORS JOURNAL,22(8),8196-8205. |
| MLA | Yang, Lei,et al."Vision-Based Power Line Segmentation With an Attention Fusion Network".IEEE SENSORS JOURNAL 22.8(2022):8196-8205. |
入库方式： OAI收割
来源：自动化研究所
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