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

作　　者：陈玲玲 李柏承[1] 张大伟[1] 杨涵 吴春波 CHEN Lingling;LI Baicheng;ZHANG Dawei;YANG Han;WU Chunbo(School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China)

机构地区：[1]上海理工大学光电信息与计算机工程学院,上海200093

出　　处：《光学仪器》2023年第2期18-25,共8页Optical Instruments

基　　金：国家自然科学基金(62005165)。

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。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-18Φ-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.

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 

分 类 号：TN913.7[电子电信—通信与信息系统]