## 1 Introduction
Nevertheless, these methods usually focus only on the detection accuracy of the network [29; 30; 31], ignoring the need for computational efficiency and the real-time performance of anomalous intrusion detection systems in metro tunnels. The large amount of data that need to be processed to provide the comprehensive and thorough monitoring of metro tunnels requires computationally efficient methods. In addition, real-time monitoring is crucial for intrusion detection systems in metro tunnels, as delayed responses can lead to significant safety risks. Real-time anomaly detection helps to prevent accidents, improve maintenance schedules, and optimize operational efficiency. Several lightweight networks have been proposed to monitor marine ecosystems via fiber optic signals [32] or structural damages via guided wave signals [33; 34], which have a high computational efficiency and a high accuracy. However, the actual underground tunnel anomaly signals are characterized by complex environmental noise, and the anomaly signals are easily drowned out by the environmental noise (e.g., tunnel-boring machines, fans, manual work, and metro running in the distance); the aforementioned lightweight networks capturing high-level features through deep architecture do not perform well in these scenarios. Therefore, according to the characteristics of tunnel intrusion signals, the objective of this paper is to design a lightweight network based on temporal convolution and a compact residual network architecture, without the need to stack multiple layers in order to generate high-level features. Compared to existing state-of-the-art networks, the accuracy, precision, and recall are expected to be improved to more than 90%; the number of parameters reduced by more than 30%; and the inference time reduced by 50% in order to meet the demand for efficient and accurate tunnel anomaly detection in industrial environments. Considering the above objectives, this paper proposes a lightweight deep learning model, the Temporal Efficient Residual Network (TERsNet), for the detection of anomalous intrusion signals. The overall system of fiber optic sensor external intrusion detection is shown in Figure 1. Taking into account the sensitivity, stability, and practicality, we adopt the optical fiber acceleration sensor based on a Michelson interferometer to collect the signal of abnormal intrusion in metro tunnels. First, the signals obtained from the optic fiber accelerometers are converted into short-time Fourier transform (STFT) features. The features are then fed into TEResNet for training and detection. Unlike most 2D convolutional methods, which require deep architecture to cover low- and high-frequency domains, our method leverages temporal convolutions in conjunction with a compact residual network architecture. Specifically, the network integrates all low-level features into the formation of high-level features in subsequent layers. In this way, it effectively exploits the informative features present in the lower layers, thus eliminating the need for stacking multiple layers to generate high-level features. An excellent performance is achieved even with a relatively limited number of layers. The experiments were conducted in the construction environment of the Guangzhou Metro, where we built a dataset containing 6948 signal segments, representing various intrusion scenarios. The results demonstrate that TEResNet not only surpasses traditional intrusion detection methods, but also outperforms state-of-the-art deep learning models. With only 48,009 learnable parameters, it achieves a high detection accuracy and robustness, making it an efficient and practical solution for metro tunnel intrusion detection. Our contributions are threefold, as follows:  
1. A novel lightweight architecture--TEResNet--is proposed. All low-level features are incorporated into the formation of high-level features in subsequent layers, thus eliminating the need to stack multiple layers in order to generate high-level features.