光通信技术2026，Vol.50Issue(4)：60-64,5.DOI:10.13921/j.cnki.issn1002-5561.2026.04.009
基于GADF-GCBAM-ResNet的DAS周界安防信号识别方法
DAS perimeter security signal recognition method based on GADF-GCBAM-ResNet
摘要
Abstract
To improve the recognition accuracy of vibration signals acquired by a distributed acoustic sensing(DAS)system,a DAS perimeter security signal recognition method is proposed.The Gramian angular difference field(GADF)is employed to convert the one-dimensional time-series vibration signals into two-dimensional images.After feature extraction via residual blocks in a residual network(ResNet),a grouped convolution block attention module(GCBAM)is applied to adjust feature weights in groups,thereby enhancing ResNet's capability to capture core features and edge details.A DAS perimeter security experimental system is built,and five types of vibration signals—climbing,tapping,trampling,running,and no intrusion—are collected for testing.Experimental results demonstrate that the proposed method achieves a recognition accuracy of 99.3%,compared with traditional models such as convolutional neural network(CNN),CNN-long short-term memory network(LSTM),and ResNet,the proposed method achieves superior performance.关键词
周界安防/分布式声波传感/残差网络/卷积注意力模块/格拉姆角差场Key words
perimeter security/distributed acoustic sensing/residual network/convolutional block attention module/Gramian angular difference field分类
信息技术与安全科学引用本文复制引用
尚秋峰,王若楠..基于GADF-GCBAM-ResNet的DAS周界安防信号识别方法[J].光通信技术,2026,50(4):60-64,5.基金项目
河北省科技计划项目(SZX2020034)资助. （SZX2020034）
