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Adaptive domain-aware network for airport runway subsurface defect detection
Automation in Construction ( IF 9.6 ) Pub Date : 2025-01-17 , DOI: 10.1016/j.autcon.2025.105969
Haifeng Li, Wenqiang Liu, Nansha Li, Zhongcheng Gui
Automation in Construction ( IF 9.6 ) Pub Date : 2025-01-17 , DOI: 10.1016/j.autcon.2025.105969
Haifeng Li, Wenqiang Liu, Nansha Li, Zhongcheng Gui
Ground-penetrating radar (GPR) is widely used in airport runway subsurface defect detection. However, variability in subsurface environments and operational frequencies of GPR systems across different airports can cause significant discrepancies in radar data, which influence defect assessments. To address this problem, this study proposes a deep learning algorithm named AD-DetNet, which is designed to maintain robust generalization performance across various airports under different radar frequency conditions. The AD-DetNet model integrates domain-specific knowledge pertinent to detecting subsurface defects in airport runways, which is suitable for various airport environments. In addition, the AD-DetNet model focuses on identifying and emphasizing common characteristics across diverse airports. Moreover, the proposed model incorporates unlabeled target-domain data during training and employs domain adaptation techniques to align features from different data domains. The results of extensive experiments demonstrate that the proposed AD-DetNet model can achieve superior generalization performance across numerous real-world airport datasets and can outperform current state-of-the-art object detection algorithms.
中文翻译:
用于机场跑道地下缺陷检测的自适应域感知网络
探地雷达 (GPR) 广泛用于机场跑道地下缺陷检测。然而,不同机场的地下环境和 GPR 系统运行频率的变化会导致雷达数据出现显着差异,从而影响缺陷评估。为了解决这个问题,本研究提出了一种名为 AD-DetNet 的深度学习算法,该算法旨在在不同雷达频率条件下保持各个机场的稳健泛化性能。AD-DetNet 模型集成了与检测机场跑道表面下缺陷相关的特定领域知识,适用于各种机场环境。此外,AD-DetNet 模型侧重于识别和强调不同机场的共同特征。此外,所提出的模型在训练过程中整合了未标记的目标域数据,并采用域适应技术来对齐来自不同数据域的特征。大量实验的结果表明,所提出的 AD-DetNet 模型可以在众多真实世界的机场数据集中实现卓越的泛化性能,并且可以超越当前最先进的目标检测算法。
更新日期:2025-01-17
中文翻译:
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用于机场跑道地下缺陷检测的自适应域感知网络
探地雷达 (GPR) 广泛用于机场跑道地下缺陷检测。然而,不同机场的地下环境和 GPR 系统运行频率的变化会导致雷达数据出现显着差异,从而影响缺陷评估。为了解决这个问题,本研究提出了一种名为 AD-DetNet 的深度学习算法,该算法旨在在不同雷达频率条件下保持各个机场的稳健泛化性能。AD-DetNet 模型集成了与检测机场跑道表面下缺陷相关的特定领域知识,适用于各种机场环境。此外,AD-DetNet 模型侧重于识别和强调不同机场的共同特征。此外,所提出的模型在训练过程中整合了未标记的目标域数据,并采用域适应技术来对齐来自不同数据域的特征。大量实验的结果表明,所提出的 AD-DetNet 模型可以在众多真实世界的机场数据集中实现卓越的泛化性能,并且可以超越当前最先进的目标检测算法。