如果你是一名资深的科研工作者你即将发表论文如何用英文表达以下内容：气体泄漏是一种常见的安全隐患其中六氟化硫的泄漏对环境和人类健康造成的危害尤为严重。一旦发生泄漏会对环境和人员造成严重的威胁因此对泄漏的检测变得非常重要。传统的气体泄漏检测方法主要包括紫外可见吸收光谱、红外吸收光谱、气体色谱等方法这些方法具有很高的精度和灵敏度但也存在着实时性差、数据处理复杂、设备费用昂贵等问题12。随着人工智能技术的

Gas leakage is a common safety hazard, and the leakage of sulfur hexafluoride poses a particularly serious threat to the environment and human health. Once a leakage occurs, it can pose a serious threat to the environment and personnel, making detection of leakage extremely important. Traditional gas leak detection methods mainly include ultraviolet-visible absorption spectroscopy, infrared absorption spectroscopy, gas chromatography, and other methods, which have high accuracy and sensitivity but also have problems such as poor real-time performance, complex data processing, and expensive equipment costs [1,2].

With the rapid development of artificial intelligence technology, deep learning technology has been widely used in image recognition, natural language processing, and other fields, among which deep neural networks have extensive applications in image processing. In the industrial production process, machine vision technology is widely used in product quality inspection, equipment fault detection, and other aspects, and the development of deep learning technology has greatly improved the performance of machine vision technology. Using deep learning for gas leak detection has become a research hotspot. Xu [3] proposed a gas leak detection method based on Faster-RCNN and experimentally verified the effectiveness of the proposed method. Lu [4] proposed an online gas leak detection method based on Gaussian mixture model, which achieved a higher F1 score compared with other algorithms. Huang [5] proposed a gas leak detection method based on Yolov4, and through simulating the construction of a gas leak experimental platform, the model achieved a detection accuracy of over 82%. Although the above-mentioned deep learning-based gas leak detection methods have achieved certain results, their detection performance is low and cannot meet the real-time and accuracy requirements of practical working conditions.

To address the problem of low performance of existing gas leak detection methods, this paper proposes an improved model based on MobileNetV3. The SA attention mechanism module used in this paper has the ability to simultaneously use channel attention and spatial attention. Among them, channel attention aggregates feature information on the channel dimension of the feature map tensor, while spatial attention aggregates feature information on the spatial dimension of each channel of the feature map tensor, which can highlight key features. Using transfer learning technology, the improved model is trained using a simulated gas leak spectrogram dataset. Although using the SA module will increase the parameter amount of the model, transferring the pre-trained learned feature information to the model's convolutional layer and some fully connected layers means that the model does not need to learn the weight values of each layer from scratch, which can reduce the time required for model detection. Comparative experiments show that the improved MobileNetV3 network model performs better than the MobileNetV3 network model, VGG-16 network model, and AlexNet network model in gas leak fault detection tasks. At the same time, the experimental results of this paper also provide useful reference and inspiration for the application of deep learning technology in gas leak fault detection field