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UAV Life Search Method Based on Multi-sensor Fusion
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[1]LI Minghui,MA Wenkai,ZHOU Yimin,et al.UAV Life Search Method Based on Multi-sensor Fusion[J].Journal of Zhengzhou University (Engineering Science),2023,44(02):61-67.[doi:10.13705/j.issn.1671-6833.2023.02.003]
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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 44卷 Number of periods: 2023 02 Page number: 61-67 Column: Public date: 2023-02-27
Title:
UAV Life Search Method Based on Multi-sensor Fusion
Author(s):
LI Minghui1;  MA Wenkai1;  ZHOU Yimin2;  YE Lingjian2
1.School of Mechanical and Electrical Engineering of Shaanxi University of Science and Technology, Xi’an 710016, 2.Shaanxi, Shenzhen Institute of Advanced Technology, China Academy of Sciences, Shenzhen, Guangdong 518055

Keywords:
data fusion;  infrared image features;  audio features;  discriminant correlation analysis (DCA);  UAV life search methods
CLC:
TP391. 4
DOI:
10.13705/j.issn.1671-6833.2023.02.003
Abstract:
In order to cope with the unstable conditions of single sensor for life detection in the fields and disaster areas, a life search method was proposed based on multi-sensor information fusion. Firstly, ResNeXt networks with different structures were constructed to extract features with different dimensional information. Deep features of audio Mel-scale Frequency Cepstral Coefficients were extracted using a one-dimensional ResNeXt network, and deep features of the infrared images were extracted using a two-dimensional ResNeXt network. Secondly, the two high-dimensional features were fused by dimensionality reduction using discriminant correlation analysis (DCA) to take into account the correlation and category of different features in order to obtain richer environmental information, thus improving the life search accuracy. Finally, the fused features were fed into a support vector machine classifier for decision making in life recognition. A bimodal dataset of audio and images with correlation was created and the proposed method was experimentally compared and analyzed in this dataset to evaluate the search performance of the proposed method. The experimental results demonstrated that the proposed method could outperform other traditional methods in feature extraction and feature fusion, and the multi-sensor fusion recognition accuracy could reach 98. 7%, which proved that the method could effectively improve the accuracy of human detection in special scenes, and the performance of multi-sensor fusion based human detection was higher than that with single sensor.
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