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English(EN) TriView-YOLO: Early Multi-View Fusion for Ground Penetrating Radar Cavity Detection in Soft, High-Water-Content Soils

TriView-YOLO模型提升了在复杂土壤条件下空洞的检测能力

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了TriView-YOLO，这是一种新颖的深度学习模型，旨在检测具有挑战性的软土高含水量土壤中的地下空洞。该模型采用多视图融合方法，整合了探地雷达数据的三个不同视角，以提高检测精度。TriView-YOLO主要在泰国曼谷的路面调查中进行了测试，平均精度均值（mAP50）达到0.558，证明了其在传统方法难以应对的条件下的有效性。 AI

影响 这项研究通过更准确地检测地质条件复杂的地下空洞，有望提高基础设施的安全性。

排序理由 该集群包含一篇详细介绍新模型及其评估的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

TriView-YOLO模型提升了在复杂土壤条件下空洞的检测能力

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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Suphawut Thawinutchokaudom, Sompote Youwai, Warat Kongkitkul, Mitsumasa Yamashina, Jose M. D. S. Rodrigues Neto, Jun Shinohara ·

    TriView-YOLO：软土高含水量条件下探地雷达空洞检测的早期多视图融合

    arXiv:2608.09522v1 Announce Type: new Abstract: Automated detection of subsurface cavities from Ground Penetrating Radar (GPR) is most difficult in soft, high-water-content ground, where conductive, water-saturated soil attenuates the signal and degrades cavity reflections, yet t…

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