基于Yolov3-Arcface的有效电力工人识别方法
Qinming Liu1, Fangzhou Hao1, Qilin Zhou1
1Tianhe Power Supply Bureau of Guangzhou Power Supply Bureau, Guangdong Power Co. Ltd., Guangzhou, 510000, China.
Heliyon
|February 26, 2024
概括
本研究介绍了一种高效的电力工人识别方法,使用改进的YOLOv3模型. 该方法提高了识别速度和准确性,以提高电力行业的安全性.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 电气工程 电气工程
背景情况:
- 配电室的电力工作人员的检测模型通常效率低,复杂度高.
- 准确的人员识别对于电气基础设施的安全和运营效率至关重要.
研究的目的:
- 开发一种高性能,轻量级的电力工人识别方法.
- 提高分发室环境中工人检测的效率和准确性.
主要方法:
- 改进的YOLOv3模型被用于对象检测.
- 使用ArcFace损失函数作为目标框的坐标回归损失.
- 用维度集群来设置模板框用于界限框预测.
主要成果:
- 改进的YOLOv3模型展示了高性能和轻量级的架构.
- 提出的电力工人识别方法实现了高速识别过程,并取得了准确的结果.
- 实验验证证证实了该方法的有效性,检测性能和稳定性.
结论:
- 开发的电力工人识别方法比现有模型有了显著的改进.
- 该方法为在配电室中识别电力工人提供了强大而高效的解决方案.
- 这项工作有助于加强电力行业的安全和运营监控.
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