可视化与非可视化特征融合超声3D目标识别研究

doi:10.16180/j.cnki.issn1007-7820.2019.05.002

Abstract

Abstract:

Nowadays, the main method of ultrasonic 3D target recognition was using sensors to obtain one or more one-dimensional echo in space and getting the 3D information of the target body by signal processing to realize the 3D target recognition. These methods generally exist the problem of low recognition rate and poor robustness,which restrict the popularization and application of this technology. In this paper, an ultrasonic 3D target recognition method based on visual and non-visual feature fusion was proposed. This method combined the target body echo signal processing method with the synthetic aperture method, carried out data fusion of the extracted target information in the feature layer and realized the classification recognition by the BP neural network, by which the shortage of existing methods can be significantly improved. Experiments on three kinds of artificial targets showed that the method can significantly improve the 3D recognition rate of the defect which can be kept above 90%, and the robustness was also improved obviously.

Key words: data fusion, ultrasonic, 3D target recognition, synthetic aperture, visual, non-visual, feature extraction

CLC Number:

TP391

SONG Shoupeng,SHEN Jingjing,LU Cuie. Research on Ultrasonic 3D Target Recognition Based on Visual and Non-Visual Feature Fusion[J].Electronic Science and Technology, 2019, 32(5): 5-6.

Figures/Tables 7

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References 20

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	圆度	偏心率	边界能量/×10⁷	梯度均值	矩形度
A	3.3	1.9	4.631 4	2.112 2	0.89
B	1.6	2.1	4.120 9	3.235 1	0.61
C	2.7	1.3	5.048 3	2.000 4	0.72

	1	2	3	4	5	6	7
A	1.744 8	5.987 2	22.734 3	21.146 3	43.980 9	26.631 3	43.087 0
B	1.770 7	7.127 7	22.590 6	19.756 4	41.930 1	23.631 3	41.486 1
C	1.774 7	7.135 8	24.359 5	21.473 3	45.403 4	25.345 7	44.945 1

	非可视化特征提取识别率	可视化特征提取识别率	串行融合算法识别率	并行融合算法识别率
A	66.7%	75%	91.7%	91.7%
B	66.7%	83.3%	91.7%	100%
C	75%	66.7%	83.3%	91.7%

Research on Ultrasonic 3D Target Recognition Based on Visual and Non-Visual Feature Fusion

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