TIGNet: Text-Image Guided Network for Airport Runway Subsurface Defect Detection
Li, Nansha Pan, Yanling Li, Haifeng Liu, Ji Gui, Zhongcheng Koshekov, Kayrat Temirbaevich Song, Dezhen
Li, Nansha
Pan, Yanling
Li, Haifeng
Liu, Ji
Gui, Zhongcheng
Koshekov, Kayrat Temirbaevich
Song, Dezhen
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Department
Robotics
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Type
Journal article
Date
2025
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Language
English
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Abstract
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 within 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 eleven airports demonstrate that TIGNet achieves superior performance over state-of-the-art methods in detection accuracy, false positive reduction, and cross-domain generalization. Specifically, our method achieves F1 score at 89%, 82%, 90%, and 92% for four types of subsurface features (i.e. gap, crack, subsidence and rebar), respectively, which indicate strong application potential in runway inspection.
Citation
N. Li et al., "TIGNet: Text–Image Guided Network for Airport Runway Subsurface Defect Detection," in IEEE Transactions on Geoscience and Remote Sensing, vol. 63, pp. 1-17, 2025, Art no. 5108617, doi: 10.1109/TGRS.2025.3620993.
Source
IEEE Transactions on Geoscience and Remote Sensing
Conference
Keywords
Airport runway inspection, ground-penetrating radar, learnable text embedding, multimodal detection network, subsurface defect
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Publisher
IEEE