Invisible Gas Detection: An RGB-Thermal Cross Attention Network and A New Benchmark
This work addresses gas leakage detection for industrial safety, providing a new benchmark and method, but it is incremental as it builds on existing RGB-thermal fusion approaches.
The paper tackles the problem of detecting invisible gas leaks using thermal infrared images by proposing an RGB-Thermal Cross Attention Network (RT-CAN) that integrates texture from RGB and gas area from thermal images, achieving state-of-the-art performance with improvements of 4.86% in accuracy, 5.65% in IoU, and 4.88% in F2 metrics over existing methods.
The widespread use of various chemical gases in industrial processes necessitates effective measures to prevent their leakage during transportation and storage, given their high toxicity. Thermal infrared-based computer vision detection techniques provide a straightforward approach to identify gas leakage areas. However, the development of high-quality algorithms has been challenging due to the low texture in thermal images and the lack of open-source datasets. In this paper, we present the RGB-Thermal Cross Attention Network (RT-CAN), which employs an RGB-assisted two-stream network architecture to integrate texture information from RGB images and gas area information from thermal images. Additionally, to facilitate the research of invisible gas detection, we introduce Gas-DB, an extensive open-source gas detection database including about 1.3K well-annotated RGB-thermal images with eight variant collection scenes. Experimental results demonstrate that our method successfully leverages the advantages of both modalities, achieving state-of-the-art (SOTA) performance among RGB-thermal methods, surpassing single-stream SOTA models in terms of accuracy, Intersection of Union (IoU), and F2 metrics by 4.86%, 5.65%, and 4.88%, respectively. The code and data can be found at https://github.com/logic112358/RT-CAN.