Super-Resolution for Gas Distribution Mapping: Convolutional Encoder-Decoder Network
Super-Resolution for Gas Distribution Mapping: Convolutional Encoder-Decoder Network
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DOI：
10.1109/isoen54820.2022.9789555
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发表时间：
2022-05
期刊：
影响因子：
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通讯作者：
N. Winkler;H. Matsukura;P. Neumann;E. Schaffernicht;H. Ishida;A. Lilienthal
中科院分区：
文献类型：
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作者：
N. Winkler;H. Matsukura;P. Neumann;E. Schaffernicht;H. Ishida;A. Lilienthal
气体分布图对于准确了解危险环境中的气体浓度水平非常重要。一个主要的问题是现场气体传感器只能测量其特定位置的浓度。因此，必须对采样位置之间的气体分布进行建模。在本研究中，我们将稀疏分布的传感器之间的空间插值任务解释为增强图像分辨率的任务，即超分辨率。由于自动编码器被证明在这种超分辨率任务中表现良好，因此我们训练了一个卷积编码器-解码器神经网络来映射空间稀疏传感器网络上的气体分布。由于难以收集真实世界的气体分布数据和缺少地面实况，我们使用气体分布模拟器生成的合成数据来训练和评估模型。我们的研究结果表明，神经网络能够学习气体羽流的行为，并优于简单的插值技术。
Gas distribution mapping is important to have an accurate understanding of gas concentration levels in hazardous environments. A major problem is that in-situ gas sensors are only able to measure concentrations at their specific location. The gas distribution in-between the sampling locations must therefore be modeled. In this research, we interpret the task of spatial interpolation between sparsely distributed sensors as a task of enhancing an image's resolution, namely super-resolution. Because auto encoders are proven to perform well for this super-resolution task, we trained a convolutional encoder-decoder neural network to map the gas distribution over a spatially sparse sensor network. Due to the difficulty to collect real-world gas distribution data and missing ground truth, we used synthetic data generated with a gas distribution simulator for training and evaluation of the model. Our results show that the neural network was able to learn the behavior of gas plumes and outperforms simpler interpolation techniques.