<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Tunnel geological radar image has been widely used in tunnel engineering quality detection for its advantages of fast, nondestructive, continuous detection, real-time imaging, intuitive data processing and high detection accuracy. However, the traditional defect detection method, which is judged by surveyors visually, consumes energy. In order to detect the quality of tunnel engineering accurately and quickly, an improved method of void defect detection based on Faster RCNN (Regional Convolutional Neural Network) is proposed in depth learning. The image data of the tunnel geological radar is collected for annotation, which fills the blank of the defect data set in the tunnel engineering. Through the method of this paper proposed, the feature extraction is optimized to improve the performance of the detection model, and the detection accuracy of the model is verified by expert knowledge.</p></abstract>

Tunnel geological radar image has been widely used in tunnel engineering quality detection for its advantages of fast, nondestructive, continuous detection, real-time imaging, intuitive data processing and high detection accuracy. However, the traditional defect detection method, which is judged by surveyors visually, consumes energy. In order to detect the quality of tunnel engineering accurately and quickly, an improved method of void defect detection based on Faster RCNN (Regional Convolutional Neural Network) is proposed in depth learning. The image data of the tunnel geological radar is collected for annotation, which fills the blank of the defect data set in the tunnel engineering. Through the method of this paper proposed, the feature extraction is optimized to improve the performance of the detection model, and the detection accuracy of the model is verified by expert knowledge.

Research on the Tunnel Geological Radar Image Flaw Detection Based on CNN

International Journal of Advanced Network, Monitoring and Controls

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Research on the Tunnel Geological Radar Image Flaw Detection Based on CNN"}

Tunnel geological radar image has been widely used in tunnel engineering quality detection for its advantages of fast, nondestructive, continuous detection,...

Material	Dielectric constant	Velocity (mm/ns)
atmosphere	1	300
water	81	30
concrete	5-8	55-120
Sand (dry)	3-6	120-170
Sand (wet)	25-30	55-60
pitch	3-5	134-173

Research on the Tunnel Geological Radar Image Flaw Detection Based on CNN

Online veröffentlicht: 23. Feb. 2022

Seitenbereich: 44 - 53

DOI: https://doi.org/10.21307/ijanmc-2020-007

Schlüsselwörter
Tunnel Geological, Radar Image, Flaw Detection, CNN

© 2020 He Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

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DIELECTRIC CONSTANTS OF COMMON MATERIALS

Research on the Tunnel Geological Radar Image Flaw Detection Based on CNN

Online veröffentlicht: 23. Feb. 2022

Seitenbereich: 44 - 53

DOI: https://doi.org/10.21307/ijanmc-2020-007

SchlüsselwörterTunnel Geological, Radar Image, Flaw Detection, CNN

© 2020 He Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

Figure 7.

DIELECTRIC CONSTANTS OF COMMON MATERIALS

Schlüsselwörter
Tunnel Geological, Radar Image, Flaw Detection, CNN