Zhong, Y.; Li, K.; Mo, S.; Liu, X. GPR Target Recognition Based on Improved YOLOv3-SPP. Preprints2023, 2023051363. https://doi.org/10.20944/preprints202305.1363.v1
APA Style
Zhong, Y., Li, K., Mo, S., & Liu, X. (2023). GPR Target Recognition Based on Improved YOLOv3-SPP. Preprints. https://doi.org/10.20944/preprints202305.1363.v1
Chicago/Turabian Style
Zhong, Y., Site Mo and Xing Liu. 2023 "GPR Target Recognition Based on Improved YOLOv3-SPP" Preprints. https://doi.org/10.20944/preprints202305.1363.v1
Abstract
When ground-penetrating radar is used to detect targets within concrete, the location of the targets, the identification of different shapes, properties and less obvious echoes all greatly increase the interpretation time of the staff and can easily cause misjudgment of the echo images. In this paper, the ground-penetrating radar echo images (B-scan) after processing are mean filtered to eliminate the direct waves that interfere greatly with the echoes. The RFB-s structure is added to the YOLOv3-SPP network structure, while the Anchor value is optimized and the EIOU loss function is introduced. For four types of data with different shapes and properties at random target locations, three models, YOLOv3, YOLOv3-SPP and the improved YOLOv3-SPP, are used for classification and identification, and the proposed algorithm models are comprehensively evaluated using model evaluation metrics. The experimental results show that the algorithm models proposed in this paper have good recognition effect in ground-penetrating radar echo image target detection.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
