دورية أكاديمية
An effective electricity worker identification approach based on Yolov3-Arcface
| العنوان: | An effective electricity worker identification approach based on Yolov3-Arcface |
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| المؤلفون: | Qinming Liu, Fangzhou Hao, Qilin Zhou, Xiaofeng Dai, Zetao Chen, Zengyu Wang |
| المصدر: | Heliyon, Vol 10, Iss 4, Pp e26184- (2024) |
| بيانات النشر: | Elsevier, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Science (General) LCC:Social sciences (General) |
| مصطلحات موضوعية: | Power distribution room, Face recognition, YOLOv3, ArcFace, Detection performance, Science (General), Q1-390, Social sciences (General), H1-99 |
| الوصف: | To address the issues of low efficiency and high complexity of detection models for electric power workers in distribution rooms, the electric power worker identification approach is proposed. The ArcFace loss function is used as the coordinate regression loss of the target box. According to the score, the template box with the highest score is selected for prediction, which speeds up the rate of convergence. Dimensional clustering is used to set template boxes for bounding box prediction. The experimental results show that the improved YOLOv3 is a high-performance and lightweight model. The electric power worker identification approach proposed in this paper has a high-speed recognition process, accurate recognition results. The effectiveness of the approach is verified with better detection performance and robustness. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2405-8440 |
| Relation: | http://www.sciencedirect.com/science/article/pii/S2405844024022151; https://doaj.org/toc/2405-8440 |
| DOI: | 10.1016/j.heliyon.2024.e26184 |
| URL الوصول: | https://doaj.org/article/941515a1beb24cbfbeed6fb8fcfc840e |
| رقم الأكسشن: | edsdoj.941515a1beb24cbfbeed6fb8fcfc840e |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 24058440 |
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| DOI: | 10.1016/j.heliyon.2024.e26184 |