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Semi-supervised learning for optical fiber sensor road intrusion signal detection.

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 N+1 classes, and the generator and discriminator are trained on the N 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.