基于深度学习的接地桩检测系统

Jun Zhang, Miao Jin, Zhiwei Guo, Jian-xing Li, Tianfu Huang, Xiwen Chen, Zhuo Chen, Bing Lu, Wei Zhou, Zijuan Guo
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引用次数: 0

摘要

国家电网安全可靠的供电给我们的生活带来了便利。同时,它在国家的建设和发展中也起着核心作用。然而,国家电网的工作环境处于高压下。为防止人身触电、损坏设备和线路、防止火灾和雷电、防止静电伤害,保证电力系统正常运行,工作人员必须按照电力操作规范安装接地桩。针对这一问题,本文提出了一种基于深度学习网络的接地桩检测系统。首先,摄像头可以实时获取这些监控区域的图像。然后将这些图像传输到接地桩检测系统进行检测。如果发现工人没有按照规范在监控区域内安装接地桩,将给予警告。目前还没有针对接地桩检测的研究。所以我们创建了自己的数据集。通过实验,我们的系统在我们的数据集上达到了92.00%的准确率,97.50%的准确率和13.5%的虚警率。
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Grounding Pile Detection System based on Deep Learning
The safe and reliable power supply provided by State Grid gives convenience for our life. At the same time, it also plays a central role in the construction and development of country. However, the working environment of State Grid is under high voltage. In order to prevent personal electric shock, damage equipment and lines, prevent fire and lightning, prevent electrostatic damage and ensure the power system operation, the staff must install grounding piles according to the power operation specification. To tackle this problem, this paper proposes a grounding pile detection system based on deep learning network. First, cameras can acquire images of these monitored areas in real time. Then, these images are transmitted to the grounding pile detection system for detection. A warning will be given if it is found that workers have not installed the grounding piles in the monitored areas in accordance with the specifications. At present, there is no research on grounding pile detection. So we created our own dataset. Through experiments, our system achieves 92.00% accuracy, 97.50% accuracy and 13.5% false alarm rate in our dataset.
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