Liu Qinming, Hao Fangzhou, Zhou Qilin, Dai Xiaofeng, Chen Zetao, Wang Zengyu
Tianhe Power Supply Bureau of Guangzhou Power Supply Bureau, Guangdong Power Co. Ltd., Guangzhou, 510000, China.
Heliyon. 2024 Feb 14;10(4):e26184. doi: 10.1016/j.heliyon.2024.e26184. eCollection 2024 Feb 29.
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.
为解决配电室电力工人检测模型效率低、复杂度高的问题，提出了电力工人识别方法。采用ArcFace损失函数作为目标框的坐标回归损失。根据得分选择得分最高的模板框进行预测，加快了收敛速度。采用维度聚类为边界框预测设置模板框。实验结果表明，改进后的YOLOv3是一个高性能、轻量级的模型。本文提出的电力工人识别方法具有高速的识别过程、准确的识别结果。该方法的有效性通过更好的检测性能和鲁棒性得到了验证。
