An intrusion behavior recognition method based on deep learning is proposed in this paper in order to improve the recognition accuracy of raster perimeter intrusion behavior. The Mach–Zehnder fiber optic interferometer was used to collect the external vibration signal sensing unit, capture the external vibration signal, use the cross-correlation characteristic method to obtain the minimum frame length of the fiber vibration signal, and preprocess the intrusion signal according to the signal strength. The intrusion signals were superimposed and several sections of signals were intercepted by fixed window length; the spectrum information is obtained by Fourier transform of the intercepted stationary signals. The convolution neural network was introduced into the pattern recognition of the intrusion signals in the optical fiber perimeter defense zone, and the different characteristics of the intrusion signals were extracted, so as to realize the accurate identification of different intrusion signals. Experimental results showed that this method was highly sensitive to intrusion events, could effectively reduce the false alarm rate of intrusion signals, and could improve the accuracy and efficiency of intrusion signal recognition.

中文翻译：

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深度学习方法中光栅周边入侵行为的模式识别

为了提高光栅周边入侵行为的识别精度，提出了一种基于深度学习的入侵行为识别方法。Mach–Zehnder光纤干涉仪用于收集外部振动信号传感单元，捕获外部振动信号，使用互相关特性法获得光纤振动信号的最小帧长，并根据信号强度。入侵信号被叠加，信号的多个部分被固定的窗口长度截取；频谱信息是通过对截获的平稳信号进行傅立叶变换获得的。卷积神经网络被引入到光纤周界防御区内的入侵信号的模式识别中，提取入侵信号的不同特征，以实现对不同入侵信号的准确识别。实验结果表明，该方法对入侵事件高度敏感，可以有效降低入侵信号的误报率，提高入侵信号识别的准确性和效率。