当前位置: X-MOL 学术Comput. Aided Civ. Infrastruct. Eng. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Evidential transformer for buried object detection in ground penetrating radar signals and interval‐based bounding box
Computer-Aided Civil and Infrastructure Engineering ( IF 8.5 ) Pub Date : 2025-01-08 , DOI: 10.1111/mice.13417
Zheng Tong, Yiming Zhang, Tao Ma

Three‐dimensional (3D) buried object detection using ground penetrating radar (GPR) benefits from the powerful capacity of image‐wise deep neural networks. However, it still faces the challenge of information loss from raw GPR signals to two‐ and three‐dimensional images, such as the frequency‐domain information loss when normalizing GPR signals into gray‐scale images and spatial information loss when using stacked B‐ and C‐scan images to replace raw GPR signals as inputs. To solve the challenge, this study has proposed an ENNreg‐transformer model, directly using raw 3D GPR signals to perform buried object detection. In the proposed model, 3D GPR signals are first converted into sequential voxelization to obtain spatiotemporal features. The features are then aggregated by an intuition‐guided feature aggregation layer to simulate the expert behavior to analyze 3D GPR data. Finally, an evidential detection header outputs 3D interval‐based bounding boxes for buried object detection. The experiment on two 3D GPR road datasets demonstrates that the proposed model exceeds other state‐of‐the‐art models on the tasks thanks to raw 3D signals and intuition‐guided feature aggregation. In addition, the interval‐based bounding box represents the spatial bounding‐box uncertainty, which derives from the inherent limitations of GPR and deep networks.

中文翻译:


探地雷达信号中埋地物体检测的证据变压器和基于间隔的边界框



使用探地雷达 (GPR) 进行三维 (3D) 埋地物体检测受益于图像深度神经网络的强大功能。然而,它仍然面临着从原始 GPR 信号到二维和三维图像的信息丢失的挑战,例如将 GPR 信号归一化为灰度图像时的频域信息丢失以及使用堆叠的 B 和 C 扫描图像替换原始 GPR 信号作为输入时的空间信息丢失。为了解决这一挑战,本研究提出了一种 ENNreg-transformer 模型,直接使用原始 3D GPR 信号进行埋地物体检测。在所提出的模型中,首先将 3D GPR 信号转换为顺序体素化以获得时空特征。然后,这些特征由直觉引导的特征聚合层聚合,以模拟专家行为以分析 3D GPR 数据。最后,证据检测标头输出基于 3D 间隔的边界框,用于埋地物体检测。在两个 3D GPR 道路数据集上的实验表明,由于原始 3D 信号和直觉引导的特征聚合,所提出的模型在任务上超越了其他最先进的模型。此外,基于间隔的边界框表示空间边界框的不确定性,这源于 GPR 和深度网络的固有局限性。
更新日期:2025-01-08
down
wechat
bug