基于YOLOv3的特定电力作业场景下的违规操作识别算法
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丘浩(1990-),男,硕士,工程师,主要从事电力系统运行与分析研究;E-mail:hq-ferd5689@qq.com

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TM73

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广西电网有限责任公司科技项目(GXKJXM20190276)


Illegaloperation recognition algorithm based on YOLOv3 in specific power operation scenario
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    摘要:

    电网作业常处于高空、高压等危险环境,此类环境常常为电力作业人员的安全带来威胁。仅靠人力监管常会出现监管不力的情况,现有的目标检测算法也只能进行简单的安全识别,无法根据特定的电力作业场景识别违规操作行为。针对这一问题,提出一种基于 YOLOv3的特定电力作业场景下的违规操作识别算法,选用 YOLOv3算法进行目标检测,同时融入场景识别机制,并引用交并比设定逻辑判断函数,检测特定场景下电力作业的违规操作行为。以电焊作业场景为例进行实验验证,实验结果表明,该模型的检测精确率为82.15%,证明了该方法的有效性,同时也对后续优化该模型提出了几点建议。

    Abstract:

    Power grid are often operated in dangerous scenes such as high altitudes and high voltages. The scenes pose threats to the safety of electric power operators. Relying only on human supervision alone often leads to inadequate supervision. Existing target detection algorithms can only perform simple safety identification and can not identify illegal operations based on specific power operation scenarios. To solve this problem, this paper proposes an illegal operation recognition algorithm based on YOLOv3 in the specific power operation scenario. The YOLOv3 algorithm is selected for target detection incorporating the scene recognition mechanism contemporarily. The logic judgment function is set by reference to the intersection over union to detect the violation of power operations in specific scenarios. After taking the welding scene as an example for experimental verification, the results show the detection accuracy of this model is calculated to be 82.15%, which proves the effectiveness of the method. Meanwhile, this paper also puts forward several suggestions for subsequent optimization of the model.

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丘浩,张炜,彭博雅,等.基于YOLOv3的特定电力作业场景下的违规操作识别算法[J].电力科学与技术学报,2021,36(3):195-202.
Qiu Hao, Zhang Wei, Peng Boya, et al. Illegaloperation recognition algorithm based on YOLOv3 in specific power operation scenario[J]. Journal of Electric Power Science and Technology,2021,36(3):195-202.

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  • 在线发布日期: 2021-08-26
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