Reservoir-enhanced Segment Anything Model for Subsurface Diagnosis

• URL: http://arxiv.org/abs/2504.18802v1
• Date: Sat, 26 Apr 2025 05:13:22 GMT
• Title: Reservoir-enhanced Segment Anything Model for Subsurface Diagnosis
• Authors: Xiren Zhou, Shikang Liu, Xinyu Yan, Yizhan Fan, Xiangyu Wang, Yu Kang, Jian Cheng, Huanhuan Chen,
• Abstract summary: Urban roads and infrastructure, vital to city operations, face growing threats from subsurface anomalies like cracks and cavities.<n>Ground Penetrating Radar (GPR) effectively visualizes underground conditions employing electromagnetic (EM) waves.<n>However, accurate anomaly detection via GPR remains challenging due to limited labeled data, varying subsurface conditions, and indistinct target boundaries.<n>We propose the Reservoir-enhanced Segment Anything Model (Res-SAM) to exploit both visual discernibility and wave-changing properties of GPR data.
• Score: 24.223897705640105
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Urban roads and infrastructure, vital to city operations, face growing threats from subsurface anomalies like cracks and cavities. Ground Penetrating Radar (GPR) effectively visualizes underground conditions employing electromagnetic (EM) waves; however, accurate anomaly detection via GPR remains challenging due to limited labeled data, varying subsurface conditions, and indistinct target boundaries. Although visually image-like, GPR data fundamentally represent EM waves, with variations within and between waves critical for identifying anomalies. Addressing these, we propose the Reservoir-enhanced Segment Anything Model (Res-SAM), an innovative framework exploiting both visual discernibility and wave-changing properties of GPR data. Res-SAM initially identifies apparent candidate anomaly regions given minimal prompts, and further refines them by analyzing anomaly-induced changing information within and between EM waves in local GPR data, enabling precise and complete anomaly region extraction and category determination. Real-world experiments demonstrate that Res-SAM achieves high detection accuracy (>85%) and outperforms state-of-the-art. Notably, Res-SAM requires only minimal accessible non-target data, avoids intensive training, and incorporates simple human interaction to enhance reliability. Our research provides a scalable, resource-efficient solution for rapid subsurface anomaly detection across diverse environments, improving urban safety monitoring while reducing manual effort and computational cost.

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