TY - GEN
T1 - Improved SSD Framework for Automatic Subsurface Object Indentification for GPR Data Processing
AU - Wang, Zhen
AU - Lan, Tian
AU - Qu, Xiaodong
AU - Gao, Sheng
AU - Yu, Zhichao
AU - Yang, Xiao Peng
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Problems such as high attenuation cause the hyperbolic characteristics of B-scan being interfered by clutter in ground penetrating radar (GPR). To address this challenge, this paper introduces deep learning to feature extraction. Considering the similarities of GPR B-scan images and digital images, a new buried target detection method is proposed, which is based on Single shot multi-box detector (SSD). On the basis of the SSD, the Feature Pyramid Networks (FPN) is added as Neck part in whole network, and the bounding box regression loss function is improved from Smooth Ll loss to Generalized Intersection over Union (GIoU) loss. The proposed method achieves detection of buried target positions and identification of materials with an accuracy rate of 92 %. Compared with the conventional SSD, the mean Average Precision (mAP) of the proposed method is improved by 8.2%. Finally, the effectiveness of the proposed GPR-Star is confirmed using experimental data.
AB - Problems such as high attenuation cause the hyperbolic characteristics of B-scan being interfered by clutter in ground penetrating radar (GPR). To address this challenge, this paper introduces deep learning to feature extraction. Considering the similarities of GPR B-scan images and digital images, a new buried target detection method is proposed, which is based on Single shot multi-box detector (SSD). On the basis of the SSD, the Feature Pyramid Networks (FPN) is added as Neck part in whole network, and the bounding box regression loss function is improved from Smooth Ll loss to Generalized Intersection over Union (GIoU) loss. The proposed method achieves detection of buried target positions and identification of materials with an accuracy rate of 92 %. Compared with the conventional SSD, the mean Average Precision (mAP) of the proposed method is improved by 8.2%. Finally, the effectiveness of the proposed GPR-Star is confirmed using experimental data.
KW - Convolutional Neural Networks (CNN)
KW - Generalized Intersection over Union (GIoU)
KW - Single Shot Multi-Box Detector(SSD)
KW - buried target detection (BTD)
KW - deep learning
KW - ground penetrating radar (GPR)
UR - http://www.scopus.com/inward/record.url?scp=85165158168&partnerID=8YFLogxK
U2 - 10.1109/Radar53847.2021.10028347
DO - 10.1109/Radar53847.2021.10028347
M3 - Conference contribution
AN - SCOPUS:85165158168
T3 - Proceedings of the IEEE Radar Conference
SP - 2078
EP - 2081
BT - 2021 CIE International Conference on Radar, Radar 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 CIE International Conference on Radar, Radar 2021
Y2 - 15 December 2021 through 19 December 2021
ER -