A lightweight network based on local-global feature fusion for real-time industrial invisible gas detection with infrared thermography

The detection of industrial invisible gas plays a vital role in preventing environmental pollution and fire accidents. Optical gas imaging (OGI) with infrared thermography is widely used in the field of gas leak monitoring and treatment by visualizing gases to quickly and accurately detect and locate the gas leak sources. However, this method still relies on manual visual inspection. Existing automatic visual gas detection methods suffer from insufficient gas feature extraction and high computational cost due to the indistinct gas features in thermal images. To address these problems, we propose a new lightweight network specialized for thermal gas feature extraction, namely GasViT, sufficiently extracting gas features at very low computational cost by local–global feature fusion. Specifically, two new feature extraction modules Multi-scale Fusion Feature Attention (MsFFA) and Multi-head Linear Self-attention (MhLSa) are proposed for GasViT. MsFFA enhances the gas local feature extraction ability by constructing multi-scale channel and spatial feature fusion maps, enabling the network to focus on more valid local information. MhLSa complements the gas global features with very low computational cost by efficiently encoding the global information of the image in terms of the innovative linear self-attention mechanism. Our experimental results on the self-made Industrial Invisible Gas (IIG) Dataset show GasViT achieves 82.7% mAP50, significantly outperforming the state-of-the-art lightweight networks. Moreover, GasViT achieves 33 FPS real-time detection with a running memory footprint of only 47.2 MB on edge computing devices, making it extremely suitable for portable and embedded detection devices than existing methods to cover gas leakage detection in complex and hazardous industrial scenarios.