## 6 Discussion  
The proposed method for detecting gas leaks uses BasicVSR++ for video super-resolution processing and combines it with the CRNN for time series analysis to achieve high-precision identification and classification of natural gas leaks. This method significantly reduces the reliance on manual inspections and traditional sensor systems, providing a scalable solution for real-time monitoring. However, a key limitation of the current system is that it cannot quickly and accurately locate the specific leak point on the pipeline. Although the system can effectively detect and classify leaks based on the gas column features captured by the infrared camera, the positioning range is limited to the area covered by the camera field of view. Inspectors still need to manually align the detected leak features with the camera field of view to match the specific point of the leak, which may delay emergency repairs and increase operational complexity. This limitation is mainly due to the lack of spatial mapping technology in the current method. Therefore, the system cannot convert the visual detection results into coordinates of the actual three-dimensional world. In addition, for the generalization of this study, since the current UUT is mostly in the form of a rectangular space, the scene built in this experiment is also a closed rectangular space. However, it is difficult to build a full-coverage experimental environment for special structures of UUT or special parts such as corners, so this experiment does not cover these special scenes. Apart from that, the experimental environment fully simulates the various environmental factors in the actual UUT, including humidity, temperature, dust, and light conditions. In the actual UUT, there is also maintenance equipment to maintain the relative stability of the environment in the UUT, which is also guaranteed in the experimental environment. So that, special circumstances where factors such as temperature and dust change due to failure of operation and maintenance equipment are not taken into consideration. In addition, the height and angle of the camera are installed and arranged with reference to the setting requirements under normal circumstances in practice. Despite this, the experimental process of this study may fail or perform poorly in the extreme cases mentioned above. Moreover, since infrared imagers can only capture gases within their parameter range, when other harmful gases in the UUT have wavelengths beyond the range, they may not be detected. However, as natural gas is the most common and widespread harmful gas in civil facilities, its detection is still the most critical.  
For the spatial positioning problem, in future work, it is possible to consider incorporating visual ranging technology to improve positioning accuracy by providing relevant depth information and 3D mapping. In addition, spatial mapping can also be considered  
Figure 14: CNN and CRNN prediction results display.  
and multi-camera setups can be utilized within a UUT to further enrich the inspection perspective and improve positioning accuracy. Regarding the generalization of the detection method, future research can further enrich the experimental scenarios and detection equipment to meet the detection needs of special parts, special structures, and other harmful gases in a UUT. In addition, the detection conditions under environmental factors changes caused by failure of operation and maintenance facilities can be tested. These works will further reduce manual intervention and, thus, achieve the goal of fully automated pipeline monitoring within a UUT.