基于新型3D深度学习架构的GPR数据图像引导路面缺陷识别
Image-Guided Pavement Defect Recognition in GPR Data with novel 3D Deep Learning Architecture
- Stanford University（斯坦福大学）
- University of Cambridge（剑桥大学）
- Technical University of Munich（慕尼黑工业大学）
- University of Hong Kong（香港大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究针对GPR自动化检测的两大挑战，构建了图像引导的3D GPR标注流程，提出含残差连接等组件的3D CNN，其路面缺陷检测性能优于基线架构。
AI中文摘要：
探地雷达（GPR）是民用与交通工程中广泛采用的非破坏性传感技术，用于地下检测。尽管其在路面状况评估中具有潜力，但GPR在自动化检测中的大规模应用存在两大关键挑战：带标注的真实数据集稀缺，以及缺乏针对三维（3D）GPR数据独特特性设计的深度学习模型。本研究首先引入一种高性价比的数据制备流程，将正射镶嵌红-绿-蓝（RGB）图像与3D GPR扫描数据相结合，生成带标注的3D GPR数据集。所提方法利用RGB与GPR数据的对齐片段，以路面表面图像为参考，将表面可见缺陷的标签转移至对应的GPR片段，从而在一段运营高速公路采集的真实数据集上实现高效的大规模标注。除数据集贡献外，本研究还提出一种专用的3D卷积神经网络（CNN）架构，融合残差连接、混合卷积核尺寸，以及深度与通道注意力机制，以增强特征表示与缺陷分类能力。该模型在路面结构的补丁与裂缝缺陷检测二分类任务上进行评估，实验结果表明，所提网络在多项评估指标上均优于基线架构， ablation研究进一步验证了所设计架构组件的有效性。本研究贡献了一种可扩展且实用的真实数据集生成方法，以及一种新型深度学习框架。
英文摘要：
Ground Penetrating Radar (GPR) is a widely adopted non-destructive sensing technology for subsurface inspection in civil and transportation engineering. Despite its potential for pavement condition assessment, the large-scale application of GPR in automated inspection has two key challenges: the scarcity of annotated real-world datasets and the lack of deep learning models designed for the unique characteristics of 3-Dimensional (3D) GPR data. This study addresses these limitations by firstly introducing a cost-effective data preparation pipeline that integrates orthomosaic Red Green Blue (RGB) imagery with 3D GPR scans to generate annotated 3D GPR datasets. The proposed method uses the aligned segments of RGB and GPR data, using pavement surface images as a reference to transfer labels of surface-visible defects to corresponding GPR segments, enabling efficient large-scale annotation in a real-world dataset collected on a highway section under operation. In addition to the dataset contribution, we propose a specialised 3D Convolutional Neural Network (CNN) architecture incorporating residual connections, mixed convolutional kernel sizes, and both depthwise and channelwise attention mechanisms to enhance feature representation and defect classification. The model is evaluated on binary classification tasks for detecting patch and crack defects in pavement structures. Experimental results demonstrate that the proposed network outperforms baseline architectures across multiple evaluation metrics. Ablation studies further confirm the effectiveness of the designed architectural components. This work contributes a scalable and practical method for real-world dataset generation, along with a novel deep learning framework.
