Sharma Ashutosh, Khullar Vikas, Kansal Isha, Chhabra Gunjan, Arora Priya, Popli Renu, Kumar Rajeev
Business School, Henan University of Science and Technology, Luoyang 471300, China.
Department of Informatics, School of Computer Science, University of Petroleum and Energy Studies, Dehradun 248007, Uttarakhand, India.
Sensors (Basel). 2024 Sep 11;24(18):5904. doi: 10.3390/s24185904.
The identification of gas leakages is a significant factor to be taken into consideration in various industries such as coal mines, chemical industries, etc., as well as in residential applications. In order to reduce damage to the environment as well as human lives, early detection and gas type identification are necessary. The main focus of this paper is multimodal gas data that were obtained simultaneously by using multiple sensors for gas detection and a thermal imaging camera. As the reliability and sensitivity of low-cost sensors are less, they are not suitable for gas detection over long distances. In order to overcome the drawbacks of relying just on sensors to identify gases, a thermal camera capable of detecting temperature changes is also used in the collection of the current multimodal dataset The multimodal dataset comprises 6400 samples, including smoke, perfume, a combination of both, and neutral environments. In this paper, convolutional neural networks (CNNs) are trained on thermal image data, utilizing variants such as bidirectional long-short-term memory (Bi-LSTM), dense LSTM, and a fusion of both datasets to effectively classify comma separated value (CSV) data from gas sensors. The dataset can be used as a valuable source for research scholars and system developers to improvise their artificial intelligence (AI) models used for gas leakage detection. Furthermore, in order to ensure the privacy of the client's data, this paper explores the implementation of federated learning for privacy-protected gas leakage classification, demonstrating comparable accuracy to traditional deep learning approaches.
气体泄漏的识别是煤矿、化工等各行业以及住宅应用中需要考虑的一个重要因素。为了减少对环境和人类生命的损害，早期检测和气体类型识别是必要的。本文的主要重点是通过使用多个气体检测传感器和热成像相机同时获取的多模态气体数据。由于低成本传感器的可靠性和灵敏度较低，它们不适合长距离气体检测。为了克服仅依靠传感器识别气体的缺点，在当前多模态数据集的收集过程中还使用了能够检测温度变化的热成像相机。该多模态数据集包含6400个样本，包括烟雾、香水、两者的组合以及中性环境。在本文中，卷积神经网络（CNN）在热图像数据上进行训练，利用双向长短期记忆（Bi-LSTM）、密集LSTM等变体以及两个数据集的融合，以有效分类来自气体传感器的逗号分隔值（CSV）数据。该数据集可作为研究学者和系统开发人员改进用于气体泄漏检测的人工智能（AI）模型的宝贵资源。此外，为了确保客户数据的隐私，本文探讨了用于隐私保护气体泄漏分类的联邦学习的实现，证明其准确性与传统深度学习方法相当。
