## 2 Literature Review  
### UUT Gas Leakage Detection and Computer Vision Application  
Current UUT gas leakage detection relies on sensor networks for monitoring. [PERSON] et al. [6] optimized sensor layouts via CFD simulations, reducing leakage source localization errors to 4.58 m, but they faced trade-offs between the response time and accuracy. Similarly, [PERSON] et al. [7] proposed a BI-IEnKF-coupling source term estimation model to achieve gas concentration distribution prediction. However, [PERSON] et al. [7] point out that the integration of models will create a huge computational burden. There is no guarantee of the estimation efficiency. [PERSON] et al. [8] analyzed gas diffusion patterns for sensor deployment but limited generalizability due to only one tunnel-type simulation. [PERSON] et al. [9] integrated CFD with deep learning (DL) for hydrogen-blended gas detection, improving prediction accuracy through sensor optimization but noted insufficient training data. Apart from that, it still relied on gas diffusion, where gas leakage and accumulation were already a reality. [PERSON] et al. [10] developed a sensor-dependent robotic inspection system, but lacked the ability to accurately locate the leak point in the scene. In addition, [PERSON] et al. [11] proposed a neural architecture combining spectral enhancement and a CNN to detect leakage in natural gas pipelines. However, leakage signals are susceptible to noise. Current UUT gas leak detection mainly uses sensor-based methods and fluid dynamics for positioning, which improves the detection effect of leak points while achieving unmanned operation. However, it still faces the disadvantages of insufficient positioning accuracy and over-reliance on the cumulative gas concentration threshold. For example, [PERSON] et al. [12] proposed that the sensor will only sound an alarm when the gas is at 20% of the lower explosion limit. Nevertheless, since alarm triggering occurs after sustained leakage durations, this method inherently suffers from detection latency, allowing for hazardous gas accumulation. These limitations amplify the risks, complicating emergency re-pairs and jeopardizing UUT operational safety. Recent computer vision (CV) advances enable gas leakage detection. [PERSON] et al. [12] achieved a 97% accuracy using CNN-based optical imaging, but there is the limitation of insufficient image datasets. [PERSON] et al. [13] fused LSTM-processed sensor data with CNN-analyzed thermal images for multimodal detection, but model robustness depends on extensive training data and gas mixture complexity. In addition, applying LSTM to sensors still does not get rid of the disadvantage that sensors rely on gas diffusion. Apart from that, using only a CNN to process image data abandons the temporal characteristics of the image. The mixed use of the above two points makes the overall process more complicated. Differing from [PERSON] et al. [14], methane leak size classification was treated as a video task, achieving near-perfect binary detection accuracy but facing data scarcity. At the same time, the gas leakage data used mainly come from gas leakages in open above-ground spaces, and the problem of insufficient data quality in environments such as UUTs is not discussed. These studies demonstrate the potential of CV while highlighting persistent data scarcity challenges. In addition, many studies have used CV to detect cracks or erosion in pipelines [15,16]. However, unlike gas diffusion, cracks or erosion are often static, and gas detection requires attention to the gas-column formation process and state to determine the leakage level. Based on the above, applying CV to gas leak detection can get rid of the limitation of the gas concentration. By identifying the gas column state at the leak point, a judgment can be made, which improves the detection efficiency. However, the current detection objects are often gas leak detection in general scenarios, and conventional CV detection also lacks application for UUTs.