EDPNet: A Transmission Line Ice-Thickness Recognition End-Side Network Based on Efficient Dynamic Perception
Ice-covered transmission lines seriously affect the stable operation of the power system. Deploying a recognition network for measuring the ice thickness on transmission lines within a deicing robot, and controlling the robot to perform resonant deicing, is an effective solution. In order to solve t...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/19/8796 |
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| author | Yangyang Jiao Yu Zhang Yinke Dou Liangliang Zhao Qiang Liu |
| author_facet | Yangyang Jiao Yu Zhang Yinke Dou Liangliang Zhao Qiang Liu |
| author_sort | Yangyang Jiao |
| collection | DOAJ |
| description | Ice-covered transmission lines seriously affect the stable operation of the power system. Deploying a recognition network for measuring the ice thickness on transmission lines within a deicing robot, and controlling the robot to perform resonant deicing, is an effective solution. In order to solve the problem that the existing recognition network is not suitable for an edge device, an ice-thickness recognition network for transmission lines based on efficient dynamic perception (EDPNet) is proposed. Firstly, a lightweight multidimensional recombination convolution (LMRC) is designed to split the ordinary convolution for lightweight design and extract feature information of different scales for reorganization. Then, a lightweight deep fusion module (LDFM) is designed, which combines the attention mechanism with different features to enhance the information interaction between the encoder and decoder. Then, a new dynamic loss function is adopted in the training process to guide the model to perform refined detection of ice-covered boundaries. Finally, we count the ice pixels and calculate the ice thickness. The model is deployed on an OrangePi5 Plus edge computing board. Compared with the baseline model, the maximum ice-thickness detection error is 4.2%, the model parameters are reduced by 86.1%, and the detection speed is increased by 74.6%. Experimental results show that EDPNet can efficiently complete the task of identifying ice-covered transmission lines and has certain engineering application value. |
| format | Article |
| id | doaj-art-6f76206067ab4c27bb2f03a84c3968ca |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-6f76206067ab4c27bb2f03a84c3968ca2025-08-20T01:47:41ZengMDPI AGApplied Sciences2076-34172024-09-011419879610.3390/app14198796EDPNet: A Transmission Line Ice-Thickness Recognition End-Side Network Based on Efficient Dynamic PerceptionYangyang Jiao0Yu Zhang1Yinke Dou2Liangliang Zhao3Qiang Liu4Shanxi Energy Internet Research Institute, Taiyuan 030032, ChinaShanxi Energy Internet Research Institute, Taiyuan 030032, ChinaCollege of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, ChinaShanxi Energy Internet Research Institute, Taiyuan 030032, ChinaShanxi Energy Internet Research Institute, Taiyuan 030032, ChinaIce-covered transmission lines seriously affect the stable operation of the power system. Deploying a recognition network for measuring the ice thickness on transmission lines within a deicing robot, and controlling the robot to perform resonant deicing, is an effective solution. In order to solve the problem that the existing recognition network is not suitable for an edge device, an ice-thickness recognition network for transmission lines based on efficient dynamic perception (EDPNet) is proposed. Firstly, a lightweight multidimensional recombination convolution (LMRC) is designed to split the ordinary convolution for lightweight design and extract feature information of different scales for reorganization. Then, a lightweight deep fusion module (LDFM) is designed, which combines the attention mechanism with different features to enhance the information interaction between the encoder and decoder. Then, a new dynamic loss function is adopted in the training process to guide the model to perform refined detection of ice-covered boundaries. Finally, we count the ice pixels and calculate the ice thickness. The model is deployed on an OrangePi5 Plus edge computing board. Compared with the baseline model, the maximum ice-thickness detection error is 4.2%, the model parameters are reduced by 86.1%, and the detection speed is increased by 74.6%. Experimental results show that EDPNet can efficiently complete the task of identifying ice-covered transmission lines and has certain engineering application value.https://www.mdpi.com/2076-3417/14/19/8796transmission lineedge computingimage segmentationlightweight network |
| spellingShingle | Yangyang Jiao Yu Zhang Yinke Dou Liangliang Zhao Qiang Liu EDPNet: A Transmission Line Ice-Thickness Recognition End-Side Network Based on Efficient Dynamic Perception Applied Sciences transmission line edge computing image segmentation lightweight network |
| title | EDPNet: A Transmission Line Ice-Thickness Recognition End-Side Network Based on Efficient Dynamic Perception |
| title_full | EDPNet: A Transmission Line Ice-Thickness Recognition End-Side Network Based on Efficient Dynamic Perception |
| title_fullStr | EDPNet: A Transmission Line Ice-Thickness Recognition End-Side Network Based on Efficient Dynamic Perception |
| title_full_unstemmed | EDPNet: A Transmission Line Ice-Thickness Recognition End-Side Network Based on Efficient Dynamic Perception |
| title_short | EDPNet: A Transmission Line Ice-Thickness Recognition End-Side Network Based on Efficient Dynamic Perception |
| title_sort | edpnet a transmission line ice thickness recognition end side network based on efficient dynamic perception |
| topic | transmission line edge computing image segmentation lightweight network |
| url | https://www.mdpi.com/2076-3417/14/19/8796 |
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