Earticle

초록

영어

Urban subsurface deterioration, including sinkholes, demands accurate and timely detection of underground cavities. Ground penetrating radar (GPR) offers non destructive subsurface imaging, but the large scale manual interpretation of noisy radargrams is impractical, which motivates automated detection with deep learning based object detectors. We apply YOLOv12, an attention enhanced one stage detector, to GPR based cavity detection and benchmark it against YOLOv8 under identical preprocessing and augmentation pipelines. On a public five-class dataset from The Open AI Dataset Project (AI-Hub, S. Korea), YOLOv12 achieves a mean average precision at intersection over union 0.50 of 0.940 and a mean average precision averaged over intersection over union values from 0.50 to 0.95 of 0.651, while sustaining at least 30 frames per second on a single graphics processing unit and outperforming YOLOv8 by up to 2.2 percentage points. These results show that attention based multi scale feature fusion in YOLOv12 substantially improves cavity detection performance, particularly for small and low contrast hyperbolic targets, and supports practical ground penetrating radar based monitoring of urban road infrastructure.

목차

Abstract
I. INTRODUCTION
II. RELATED WORK
III. PROPOSED METHOD
A. Architectural Design
B. Data Preprocessing
IV. EXPERIMENTAL RESULTS
A. DataSet
B. Experimental Setup
C. Results
V. CONCLUSION
ACKNOWLEDGMENT
REFERENCES

저자

• Youngho Cheon [ Dept. of Information Covergence Engineering Pusan National University Busan, Republic of Korea ]
• Seoyun Jung [ Dept. of Information Covergence Engineering Pusan National University Busan, Republic of Korea ]
• Joonho Kwon [ Graduate School of Data Science Pusan National University Busan, Republic of Korea ] Corresponding Author

참고문헌