CFDNet: Coupling Computational Fluid Dynamics With Convolutional Neural Networks for Gas Detection Using Thermal Infrared Multispectral Video

Haiyang Xiong, Liqin Cao, Du Wang, Yuhao Du, Yanfei Zhong, Shijin Wang

Abstract: Gas detection is critically important in both industrial production and environmental monitoring. Due to the absorption characteristics of gases in the infrared wavelength domain, thermal infrared multispectral video imaging provides a convenient data sensing method for rapid, large-scale gas detection. In the process of gas detection, the shape of the detected gas leakage region is often distorted by the irregular motion patterns of gases. Although some studies have considered temporal motion information, these spatial–temporal feature extractors were designed originally for regular and salient objects such as vehicles and pedestrians, but not for gas. Moreover, the problem of weak gas signal and lack of a large well-labeled dataset also hinders the development of gas detection with deep learning. Regarding the above issues, a novel gas detection network, computational fluid dynamics neural network (CFDNet), was proposed for infrared multispectral gas detection. First, to better fit the motion patterns of gas and keep the shape of the detected leakage region, a spatial–temporal fluid motion feature extractor, computational fluid dynamics (CFD) Basic Block, was proposed. CFD Basic Block diffuses and displaces high-dimensional features through a convolution based on the Navier–Stokes equations in CFDs. Second, to enhance the signal of gaseous targets, a local entropy and combination difference (LED) data feature enhancement module was designed based on the instrument characteristics and local entropy information. Finally, a simulated dataset and a transfer learning framework were built to train a deep learning gas detection model with great generalizability. Experiments show that the proposed method, CFDNet, achieves a better performance than existing approaches. On the real-world dataset used for testing, it reaches an IoU of 50.128%, a Kappa of 59.902%, and an $F1$ score of 44.885%. CFDNet demonstrates an excellent performance on keeping the shape of the detected gas leakage region under irregular motion patterns, especially for gas plumes with small-area ratio and low signal-to-noise ratio.

External IDs:dblp:journals/tgrs/XiongCWDZW25