Semi-supervised learning for optical fiber sensor road intrusion signal detection
Jun He, Xing Hu, Dawei Zhang, Yong Kong, Jing Cheng, Wenzhe Xiao
Abstract
This paper proposes a road intrusion detection model based on distributed optical fiber vibration sensors signals. Considering that the existing unsupervised classification method often has a high false alarm rate when meeting the new non-intrusion samples, we propose a one-dimensional semi-supervised generative adversarial network (1D-SSGAN) model for intrusion signal recognition. The 1D-SSGAN is composed of a generator and a discriminator. The output layer of the discriminator is mapped to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi>N</mml:mi> </mml:mrow> <mml:mo>+</mml:mo> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>1</mml:mn> </mml:mrow> </mml:math> classes, and the generator and discriminator are trained on the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi>N</mml:mi> </mml:mrow> </mml:math> class dataset. During the learning process of the generator against the discriminator, many new samples are generated based on a small number of samples, which effectively expands the datasets and assists the training of the discriminator. Experimental result analysis demonstrates the effectiveness of the proposed model.