TIGNet: Text–Image Guided Network for Airport Runway Subsurface Defect Detection

Li N. Pan Y. Li H. Liu J. Gui Z. Koshekov K. Song D.
2025 Institute of Electrical and Electronics Engineers Inc.

IEEE Transactions on Geoscience and Remote Sensing
2025 #63

Ground-penetrating radar (GPR) is widely used for detecting subsurface defects in airport runways. However, GPR data is often noisy, complex, and inconsistent due to different subsurface structures and environmental conditions across airports. These factors pose serious challenges to existing detection models, as similar features across different defect types and diverse patterns in the same type make it hard to learn stable and discriminative representations. To address these issues, this study proposes a multimodal detection framework, text–image guided network (TIGNet), which integrates GPR image with subsurface layer information and textual semantics, enhancing both feature learning and target discrimination. Furthermore, a learnable text embedding mechanism is introduced, enabling the model to adaptively refine textual features during training, rather than relying on manually designed templates. Experiments on data collected from 11 airports demonstrate that the TIGNet achieves superior performance over state-of-the-art methods in detection accuracy, false-positive reduction, and cross-domain generalization. Specifically, our method achieves an F1 -score at 89%, 82%, 90%, and 92% for four types of subsurface features (i.e., gap, crack, subsidence, and rebar), respectively, which indicates strong application potential in runway inspection.

Airport runway inspection , ground-penetrating radar (GPR) , learnable text embedding , multimodal detection network , subsurface defect

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Civil Aviation University of China, Computer Science and Technology, Tianjin, 300300, China
Chengdu Textile College, Sichuan, Chengdu, 610000, China
Shanghai Guimu Robot Company Ltd., Shanghai, 200092, China
Civil Aviation Academy, Almaty, 050039, Kazakhstan
Department of Robotics, Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, United Arab Emirates

Civil Aviation University of China
Chengdu Textile College
Shanghai Guimu Robot Company Ltd.
Civil Aviation Academy
Department of Robotics

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Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026