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| 基于轻量级网络的光纤传感振动信号识别 |
| Optical fiber sensing vibration signal recognition based on lightweight network |
| 投稿时间：2022-12-02 |
| DOI：10.3969/j.issn.1005-5630.2023.002.003 |
| 中文关键词: 卷积神经网络 轻量级网络 深度可分离卷积 光纤信号 周界安全 |
| 英文关键词:convolutional neural network lightweight network depth separable convolution optical fiber signal perimeter safety |
| 基金项目:国家自然科学基金（62005165） |
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| 摘要点击次数: 1792 |
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| 中文摘要: |
| 虽然传统卷积神经网络的识别率很高，但是其庞大的参数量会导致工业部署困难，且识别响应速度慢。引入轻量级卷积神经网络MobileNet，使用深度可分离卷积替代传统卷积，大大减少了模型参数量。以MobileNet为基准网络，实现了基于一维轻量级网络MobileNet-18的Φ-OTDR周界入侵事件识别。通过实验对比了不同结构下的网络识别率和识别速度，在保证模型的准确率不会大幅度降低的情况下，选取MobileNet-18作为最佳模型。采集了攀爬、切割、风吹、举起、拉动和走动这 6种周界光纤入侵信号。在 6种光纤入侵信号识别中，MobileNet-18达到了识别率为 98.33%，响应时间为 9.27 ms的最佳效果 |
| 英文摘要: |
| Based on the application of distributed optical fiber sensing system in the field of perimeter security monitoring, there are problems such as slow response speed and low recognition rate. Although the recognition rate of the traditional convolutional neural network is very high, its huge amount of parameters makes industrial deployment difficult and the recognition response speed is slow. This paper introduces the lightweight convolutional neural network MobileNet, which uses depth-separable convolution to replace the traditional convolution, which greatly reduces the amount of model parameters. This paper uses MobileNet as the benchmark network to implement a one-dimensional lightweight network based on MobileNet-18 Φ-OTDR perimeter intrusion event recognition, compared the network recognition rate and recognition speed under different structures through experiments, and selected MobileNet-18 as the best model under the condition that the accuracy of the model would not be greatly reduced. In the experiment, six perimeter fiber intrusion signals of climbing, cutting, wind blowing, lifting, pulling and walking were collected. Among the six types of fiber intrusion signal recognition, MobileNet-18 achieved a recognition rate of 98.33% and a response time of 9.27 ms. |
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