Multi-modal data fusion human body target identification method based on automatic control

Through multimodal data fusion and deep learning methods, the problems of sensor data heterogeneity and interference in mine fires were solved, high-precision human target recognition was achieved in dense smoke and high-temperature environments, and the robustness and real-time performance of recognition were improved.

CN120635949APending Publication Date: 2025-09-12山西科技学院
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Patent Information

Application Number
CN202510843997.5
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-06-23
Publication Date
2025-09-12

AI Technical Summary

Technical Problem

In mine fires, thick smoke and high temperature environments cause traditional multimodal fusion methods to fail, the quality of single sensor data deteriorates, and the features of human targets and thermal pseudo-targets are mixed, making it difficult to achieve high-precision identification.

Method used

A multimodal data fusion method based on automatic control is adopted. Through cross-modal data fusion of infrared thermal imaging and visible light imaging, a feature complementarity and interference suppression model based on deep neural network is built. The HOG algorithm and Canny edge detector are combined for preprocessing. Wavelet graph convolutional neural network is used for multi-scale learning. A multi-perspective fusion module is constructed and an anti-interference recognition network is trained.

Benefits of technology

Highly robust human target recognition is achieved in environments with dense smoke obscuration and thermal radiation interference, significantly improving recognition accuracy and anti-interference capabilities, ensuring the real-time and reliability of mine fire rescue.

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Abstract

The invention relates to the technical field of target identification based on multi-modal data fusion, and aims to solve the problems that an existing life detection system is difficult to effectively distinguish spatial distribution of a human body target and a hot pseudo target and cannot realize real-time synchronous analysis of multi-source heterogeneous data, so that the target positioning reliability in an extreme environment is seriously restricted. The invention provides a multi-modal data fusion human body target recognition method based on automatic control, and the method comprises the steps: constructing a feature complementation and interference suppression model based on a deep neural network through fusing the cross-modal data of infrared thermal imaging and visible light imaging, and employing a multi-scale anti-interference network based on wavelet graph convolution and an attention mechanism to recognize a human body target. The environment noise broadband interference is suppressed through the time-frequency localization characteristic, high-robustness recognition of a human body target in severe environments such as dense smoke shielding and thermal radiation interference is achieved, and accurate distinguishing of the human body and a thermal pseudo target in a complex fire scene environment is achieved.
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