Deep learning based water leakage detection for shield tunnel lining

Abstract

Shield tunnel lining is prone to water leakage, which may further bring about corrosion and structural damage to the walls, potentially leading to dangerous accidents. To avoid tedious and inefficient manual inspection, many projects use artificial intelligence (AI) to detect cracks and water leakage. A novel method for water leakage inspection in shield tunnel lining that utilizes deep learning is introduced in this paper. Our proposal includes a ConvNeXt-S backbone, deconvolutional-feature pyramid network (D-FPN), spatial attention module (SPAM). and a detection head. It can extract representative features of leaking areas to aid inspection processes. To further improve the model’s robustness, we innovatively use an inversed low-light enhancement method to convert normally illuminated images to low light ones and introduce them into the training samples. Validation experiments are performed, achieving the average precision (AP) score of 56.8%, which outperforms previous work by a margin of 5.7%. Visualization illustrations also support our method’s practical effectiveness.

Graphical abstract

Keywords

water leakage detection / deep learning / deconvolutional-feature pyramid / spatial attention

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Acknowledgements

This work is funded by the National Natural Science Foundation of China (Grant Nos. 62171114 and 52222810) and the Fundamental Research Funds for the Central Universities (No. DUT22RC (3)099).

Competing interests

The authors declare that they have no competing interests.

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