基于深度学习的输电线路下工程车辆入侵检测

Chunjiang Yan, Chuang Wang, J. Du, Hualin Fang, Yixuan Wang, Xuezhi Xiang, Xinli Guo
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引用次数: 1

摘要

提出了一种基于深度学习的两步法工程车辆入侵检测方法。第一步采用入侵检测算法识别潜在目标区域;然后将结果提供给训练好的深度卷积神经网络分类器。该方法将入侵检测方法与CNN相结合,可以有效检测出高压输电线路下工程车辆的入侵,准确率高达97.2%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Intrusion detection for engineering vehicles under the transmission line based on deep learning
A two-step method based on deep learning is proposed for the intrusion detection of engineering vehicles working under high power transmission lines. In the first step, intrusion detection algorithm is used to identify the potential target area. Then the results are supplied to a trained deep convolution neural network classifier. This way combining intrusion detection method with CNN, the invasion of the engineering vehicles under high power transmission lines can efficiently be detected up to an accuracy of 97.2 %.
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