Distributed optical fiber sensing (DOFS) technology has been widely applied in pipeline monitoring, seismic detection, and security protection due to its broad coverage, high sensitivity, and strong anti-interference capability. However, the acquired signals are typically noisy, exhibit complex temporal-spatial patterns, and contain high-dimensional categorical features, posing significant challenges for robust classification. To address these issues, this paper introduces an Inception-ResNet-based model for intrusion event recognition in DOFS systems. The Inception architecture extracts multi-scale features from complex vibration patterns, while the residual optimization of ResNet enables efficient deep feature propagation and stable training. Furthermore, to enhance model interpretability, a Grad-CAM-based mechanism is integrated to visualize class-discriminative regions in the vibration signals, revealing the patterns that most strongly influence the network's decisions. Extensive experiments demonstrate the effectiveness of the proposed approach, achieving an average classification accuracy of 92.6%, outperforming traditional deep learning networks even with significantly reduced training data. These results indicate that the interpretable Inception-ResNet framework not only accurately classifies complex one-dimensional sensing signals but also provides transparent and reliable support for practical DOFS applications.
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