基于视觉的变电站人员检测

Phi–Long H. Nguyen, V. Pham
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引用次数: 0

摘要

在现实中,需要可靠地检测未经授权进入禁止区域,如远程操作高压变电站。为此,本文提出了一种基于视觉的变电站人员检测方法。从COCO数据集、Youtube和一个真实的220kV变电站收集了4.2万多张图像,用于训练模型。采用深度学习模型EfficientDet-D1和YOLOv5-m进行迁移学习。实验结果表明,EfficientDet-D1和YOLOv5-m在220kV变电站中识别人的mAP值分别为63.2%和88.6%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Vision based People Detection in Power Substations
In reality, it is desired to reliably detect unauthorized entries for prohibited areas such as teleoperated high voltage power substations. To this end, this paper presents a vision based people detection method in power substations. More than 42,000 images were collected from the COCO dataset, Youtube and a real 220kV power substation for training models. Deep learning models EfficientDet-D1 and YOLOv5-m were employed for transfer learning. Experimental results show that the EfficientDet-D1 and YOLOv5-m can recognize people in 220kV power substations with mAP of 63.2% and 88.6%, respectively.
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