Foreign body intrusion monitoring and recognition approach in a power distribution room based on the improved YOLOv4 deep learning network
In order to avoid safety problems caused by foreign bodies such as mice that may appear in the power distribution room and by demarcating the electronic fence area for key monitoring in the video surveillance screen, a foreign body intrusion monitoring and recognition approach in a power distributio...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-01-01
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| Series: | Frontiers in Energy Research |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2022.1090033/full |
| _version_ | 1797955274473996288 |
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| author | Shenyu Chen Xiaofeng Dai Zengyu Wang Pan Zhang Zetao Chen |
| author_facet | Shenyu Chen Xiaofeng Dai Zengyu Wang Pan Zhang Zetao Chen |
| author_sort | Shenyu Chen |
| collection | DOAJ |
| description | In order to avoid safety problems caused by foreign bodies such as mice that may appear in the power distribution room and by demarcating the electronic fence area for key monitoring in the video surveillance screen, a foreign body intrusion monitoring and recognition approach in a power distribution room based on the improved YOLOv4 deep learning network is proposed. To optimize the detection effects, the YOLOv4 algorithm is improved from the aspects of network structure, frame detection, and loss function. At the same time, the channel pruning algorithm is used to prune the model to simplify the model structure. The experimental results show the effectiveness of the improved YOLOv4 deep learning network, which has high detection accuracy, fast detection speed, and takes up less space after pruning. |
| first_indexed | 2024-04-10T23:31:34Z |
| format | Article |
| id | doaj.art-3d9ddf0829ec4d44b3a1b00a65631f9b |
| institution | Directory Open Access Journal |
| issn | 2296-598X |
| language | English |
| last_indexed | 2024-04-10T23:31:34Z |
| publishDate | 2023-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj.art-3d9ddf0829ec4d44b3a1b00a65631f9b2023-01-12T05:47:41ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2023-01-011010.3389/fenrg.2022.10900331090033Foreign body intrusion monitoring and recognition approach in a power distribution room based on the improved YOLOv4 deep learning networkShenyu ChenXiaofeng DaiZengyu WangPan ZhangZetao ChenIn order to avoid safety problems caused by foreign bodies such as mice that may appear in the power distribution room and by demarcating the electronic fence area for key monitoring in the video surveillance screen, a foreign body intrusion monitoring and recognition approach in a power distribution room based on the improved YOLOv4 deep learning network is proposed. To optimize the detection effects, the YOLOv4 algorithm is improved from the aspects of network structure, frame detection, and loss function. At the same time, the channel pruning algorithm is used to prune the model to simplify the model structure. The experimental results show the effectiveness of the improved YOLOv4 deep learning network, which has high detection accuracy, fast detection speed, and takes up less space after pruning.https://www.frontiersin.org/articles/10.3389/fenrg.2022.1090033/fullpower distribution roomforeign body intrusion monitoringimproved YOLOv4channel pruning algorithmmonitoring-highway hazard |
| spellingShingle | Shenyu Chen Xiaofeng Dai Zengyu Wang Pan Zhang Zetao Chen Foreign body intrusion monitoring and recognition approach in a power distribution room based on the improved YOLOv4 deep learning network Frontiers in Energy Research power distribution room foreign body intrusion monitoring improved YOLOv4 channel pruning algorithm monitoring-highway hazard |
| title | Foreign body intrusion monitoring and recognition approach in a power distribution room based on the improved YOLOv4 deep learning network |
| title_full | Foreign body intrusion monitoring and recognition approach in a power distribution room based on the improved YOLOv4 deep learning network |
| title_fullStr | Foreign body intrusion monitoring and recognition approach in a power distribution room based on the improved YOLOv4 deep learning network |
| title_full_unstemmed | Foreign body intrusion monitoring and recognition approach in a power distribution room based on the improved YOLOv4 deep learning network |
| title_short | Foreign body intrusion monitoring and recognition approach in a power distribution room based on the improved YOLOv4 deep learning network |
| title_sort | foreign body intrusion monitoring and recognition approach in a power distribution room based on the improved yolov4 deep learning network |
| topic | power distribution room foreign body intrusion monitoring improved YOLOv4 channel pruning algorithm monitoring-highway hazard |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2022.1090033/full |
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