Ground Penetrating Radar Hyperbolic Keypoint Detection and Object Localization Based on Dual YOLOv8-pose Model

HOU Feifei; PENG Yinghao; DONG Jian; YIN Xue

doi:10.11999/JEIT240242

Volume 46 Issue 11

Nov. 2024

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HOU Feifei, PENG Yinghao, DONG Jian, YIN Xue. Ground Penetrating Radar Hyperbolic Keypoint Detection and Object Localization Based on Dual YOLOv8-pose Model[J]. Journal of Electronics & Information Technology, 2024, 46(11): 4305-4316. doi: 10.11999/JEIT240242

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HOU Feifei, PENG Yinghao, DONG Jian, YIN Xue. Ground Penetrating Radar Hyperbolic Keypoint Detection and Object Localization Based on Dual YOLOv8-pose Model[J]. Journal of Electronics & Information Technology, 2024, 46(11): 4305-4316. doi: 10.11999/JEIT240242

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Ground Penetrating Radar Hyperbolic Keypoint Detection and Object Localization Based on Dual YOLOv8-pose Model

doi: 10.11999/JEIT240242

1.
School of Automation, Central South University, Changsha 410083, China
2.
School of Electronic Information, Central South University, Changsha 410004, China

Funds: The National Natural Science Foundation of China (62406346), Hunan Provincial Natural Science Foundation (2022J30052), Changsha Natural Science Foundation (kq2208285)

Received Date: 2024-04-08
Rev Recd Date: 2024-09-12

Available Online: 2024-09-19

Publish Date: 2024-11-10

Abstract

Abstract

Ground Penetrating Radar (GPR) is identified as a non-destructive method usable for the identification of underground targets. Existing methods often struggle with variable target sizes, complex image recognition, and precise target localization. To address these challenges, an innovative method is introduced that leverages a dual YOLOv8-pose model for the detection and precise localization of hyperbolic keypoint. This method, termed Dual YOLOv8-pose Keypoint Localization (DYKL), offers a sophisticated solution to the challenges inherent in GPR-based target identification and positioning. The proposed model architecture includes two stages: firstly, the YOLOv8-pose model is employed for the preliminary detection of GPR targets, adeptly identifying regions that are likely to contain these targets. Secondly, building upon the training weights established in the first phase, the model further hones the YOLOv8-pose network. This refinement is geared towards the precise detection of keypoints within the candidate target features, thereby facilitating the automated identification and exact localization of underground targets with enhanced accuracy. Through comparison with four advanced deep-learning models— Cascade Region-based Convolutional Neural Networks (Cascade R-CNN), Faster Region-based Convolutional Neural Networks (Faster R-CNN), Real-Time Models for object Detection (RTMDet), and You Only Look Once v7(YOLOv7-face), the proposed DYKL model exhibits an average recognition accuracy of 98.8%, surpassing these models. The results demonstrate the DYKL model’s high recognition accuracy and robustness, serving as a benchmark for the precise localization of subterranean targets.
- Ground penetrating radar,
- Target detection,
- Keypoint detection,
- YOLOv8

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References(42)

References

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