###### Abstract  
An underground utility tunnel (UUT) is essential for the efficient use of urban underground space. However, current maintenance systems rely on patrol personnel and professional equipment. This study explores industrial detection methods for identifying and monitoring natural gas leaks in UUTs. Via infrared thermal imaging gas experiments, data were acquired and a dataset established. To address the low-resolution problem of existing imaging devices, video super-resolution (VSR) was used to improve the data quality. Based on a convolutional recurrent neural network (CRNN), the image features at each moment were extracted, and the time series data were modeled to realize the risk-level classification mechanism based on the automatic classification of the leakage rate. The experimental results show that when the sliding window size was set to 10 frames, the classification accuracy of the CRNN was the highest, which could reach 0.98. This method improves early warning precision and response efficiency, offering practical technical support for UUT maintenance management.  
underground utility tunnel; natural gas leak detection; basic video super-resolution reconstruction; long short-term memory network; convolutional recurrent neural network +
Footnote †: journal: Applied sciences  
Article
rely on manual patrols and gas sensors. Patrol personnel regularly conduct routine inspections inside UUTs but are unable to detect leakage of colorless gas. Gas sensors, while providing real-time monitoring, lack the ability to pinpoint leaks until gas concentrations are detectable, which hinders timely responses. Thus, meeting the monitoring needs for large-scale UUTs remains a challenge.  
Faced with the above problems, at present, the operation and maintenance phase relies heavily on manual work and sensors, and there is no efficient and accurate detection method to ensure the safe and reliable operation of a UUT. In addition, the risks and safety issues that arise during the operation and maintenance of a UUT are relatively complex, which places high requirements on inspection personnel and significantly increased operating costs [3]. To solve these problems, this paper aims to realize the real-time monitoring and grading of gas leaks in UUTs. This is achieved through the development of a novel and efficient model based on a deep convolutional neural network (CNN). An experimental setup is designed to simulate the leakage of gases in realistic scenarios, thereby constructing a comprehensive gas leakage dataset for UUTs that encapsulates a diversity of leakage conditions and involves the intricacies of real-world environments. To solve the problem of the low resolution of existing imaging devices, Basic Video Super-Resolution Reconstruction (BasicVSR++) [4] is used to process thermal imaging video data, and then data enhancement methods, such as rotation, mirroring, adding Gaussian noise, changing brightness, and adding a color block occlusion and sliding window, are used to create a dataset. Then, a convolutional recurrent neural network (CRNN) [5], based on a CNN and a long short-term memory network (LSTM) of recurrent neural network (RNN) variants, was used to model the time series data to detect and grade gas leaks in UUTs. This study enables the detection of gas leaks within UUTs, overcoming the constraints of current manual inspections and sensor-based detection methods, thereby enhancing the operation and maintenance management capabilities of UUTs.  
Figure 1: Schematic diagram of the cross-section of a UUT.