隐形气体检测:RGB 热交叉注意网络和新基准

IF 4.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Computer Vision and Image Understanding Pub Date : 2024-07-31 DOI:10.1016/j.cviu.2024.104099
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引用次数: 0

摘要

鉴于各种化学气体的剧毒性,在工业生产过程中广泛使用这些气体需要采取有效措施防止其在运输和储存过程中发生泄漏。基于热红外的计算机视觉检测技术为识别气体泄漏区域提供了一种直接的方法。然而,由于热图像的纹理较低以及缺乏开源数据集,开发高质量算法一直面临挑战。在本文中,我们提出了 GB-热像仪网络(RT-CAN),它采用 RGB 辅助双流网络架构,将 RGB 图像中的纹理信息和热图像中的气体区域信息整合在一起。此外,为了促进对隐形气体检测的研究,我们引入了 Gas-DB,这是一个广泛的开源气体检测数据库,包含约 1.3K 幅注释良好的 RGB-热图像和 8 个不同的采集场景。实验结果表明,我们的方法成功地利用了两种模式的优势,在 RGB 热图像方法中取得了最先进的(SOTA)性能,在准确率、联合交叉(IoU)和 F2 指标方面分别超过单流 SOTA 模型 4.86%、5.65% 和 4.88%。代码和数据可在以下网址找到。
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Invisible gas detection: An RGB-thermal cross attention network and a new benchmark

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.

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来源期刊
Computer Vision and Image Understanding
Computer Vision and Image Understanding 工程技术-工程:电子与电气
CiteScore
7.80
自引率
4.40%
发文量
112
审稿时长
79 days
期刊介绍: The central focus of this journal is the computer analysis of pictorial information. Computer Vision and Image Understanding publishes papers covering all aspects of image analysis from the low-level, iconic processes of early vision to the high-level, symbolic processes of recognition and interpretation. A wide range of topics in the image understanding area is covered, including papers offering insights that differ from predominant views. Research Areas Include: • Theory • Early vision • Data structures and representations • Shape • Range • Motion • Matching and recognition • Architecture and languages • Vision systems
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