Telemetry identification method, system, equipment, device and storage medium for gas imaging

By using spatial filtering algorithms and convolutional neural networks in multispectral gas imaging technology, combined with spatial differential algorithms, the target gas recognition problem under the influence of background radiation is solved, and effective identification and early warning of target gas is achieved, which is suitable for a variety of industrial scenarios.

CN115082796BActive Publication Date: 2025-03-18HEFEI INSTITUTE OF PHYSICAL SCIENCE CHINESE ACADEMY OF SCIENCES
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Patent Information

Application Number
CN202210789712.0
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2022-07-06
Publication Date
2025-03-18
Estimated Expiration
2042-07-06

AI Technical Summary

Technical Problem

Under the external field conditions, multispectral gas imaging technology is difficult to effectively identify target gases with weak radiation signals due to strong background radiation.

Method used

The spatial filtering algorithm is used to subtract background radiation, and the feature map of multispectral images is extracted through a convolutional neural network, and the multispectral features of the target area are obtained by combining the spatial differential algorithm to match and identify the target gas.

Benefits of technology

Effective identification of target gases is achieved, including positioning information, classification information and concentration information, and a portable gas early warning system is formed, suitable for online monitoring of safety production in industrial pipelines, mines, oil fields, etc.

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Abstract

The present invention provides a remote sensing identification method, system, equipment, device and storage medium for gas imaging, which belongs to the field of gas monitoring technology. The remote sensing identification method for multi-spectral gas imaging includes: obtaining a multi-spectral image of a target area, the multi-spectral image includes multiple reference band images and a target band image of the target area; obtaining a feature map of the multi-spectral image through a convolutional neural network, the feature map includes radiation temperature distribution information corresponding to multiple reference bands and target bands in the multi-spectral image; based on the radiation temperature distribution information of the multi-spectral image, the multi-spectral features of the target area are calculated by a spatial difference algorithm; based on the multi-spectral feature matching the target feature, the identification result of the target gas in the target area is obtained. The present invention adopts a spatial difference filtering algorithm to deduct the background radiation in the collected image, extract the target spectral signal, and use a neural network algorithm to identify the gas composition of the target area, and draw the gas spatial distribution in real time.
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