Invisible Gas Detection: An RGB-Thermal Cross Attention Network and A New Benchmark

TL;DR

This paper introduces RT-CAN, a novel RGB-Thermal cross attention network for invisible gas detection, supported by a new extensive dataset, Gas-DB, achieving state-of-the-art accuracy by effectively combining RGB and thermal image information.

Contribution

The paper presents a new two-stream cross attention network architecture and a large open-source dataset for improved invisible gas detection.

Findings

RT-CAN outperforms single-stream models in accuracy, IoU, and F2 metrics.

Gas-DB contains about 1.3K annotated RGB-thermal images across eight scenes.

The method effectively leverages both RGB and thermal modalities for gas detection.

Abstract

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.