Recent urban revitalisation requires advanced utility management and innovative technology to achieve high-precision utility management. This paper introduces an automated framework that surpasses traditional methods of subsurface utility detection by integrating Ground Penetrating Radar (GPR), deep learning, and Augmented Reality (AR) to provide an advanced solution for subsurface detection and visualization. GPR data is collected using a multisensory GPR device, which employs antennas operating at different frequency ranges to achieve high-resolution imaging and deep penetration. Subsequently, a Mask R-CNN deep learning model is trained using a custom dataset, integrating transfer learning and data augmentation to improve detection reliability. The results are refined through profile alignment and Non-Maximum Suppression to increase accuracy. Finally, the detected utilities are visualized through a developed AR application incorporating spatial mapping and anchoring for precise model alignment and tracking. The developed system demonstrates promising results, providing an efficient utility detection and visualization solution.

中文翻译：

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使用 GPR 和深度学习进行地下公用设施检测和增强现实可视化

最近的城市振兴需要先进的公用事业管理和创新技术来实现高精度的公用事业管理。本文介绍了一种自动化框架，它通过集成探地雷达 （GPR）、深度学习和增强现实 （AR） 来超越传统的地下公用设施检测方法，为地下检测和可视化提供先进的解决方案。GPR 数据是使用多传感器 GPR 设备收集的，该设备采用在不同频率范围内工作的天线来实现高分辨率成像和深度穿透。随后，使用自定义数据集训练 Mask R-CNN 深度学习模型，集成迁移学习和数据增强以提高检测可靠性。通过轮廓对齐和非极大值抑制 （Non-Maximum Suppression） 来优化结果，以提高准确性。最后，通过开发的 AR 应用程序可视化检测到的实用程序，该应用程序结合了空间映射和锚定，以实现精确的模型对齐和跟踪。开发的系统展示了有希望的结果，提供了高效的公用事业检测和可视化解决方案。