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Journal of Intelligent Construction 2023, 1(1): 9180004 https://doi.org/10.26599/JIC.2023.9180004
Research Article | Open Access | Issue | Published: 12 April 2023

Improved SOLOv2 detection method for shield tunnel lining water leakages

Show Author's Information Yong Feng1,  Xiaolei Zhang2,  Shijin Feng1,2( ),  Hongxin Chen2,  Yong Zhao2,  Yihan Chen1
Urban Mobility Institute, College of Transportation Engineering, Tongji University, Shanghai 201804, China
Key Laboratory of Geotechnical and Underground Engineering of Ministry of Education, Department of Geotechnical Engineering College of Civil Engineering, Tongji University, Shanghai 200092, China
Keywords:
metro shield tunnel, leakage, instance segmentation, segmenting objects by locations network v2 (SOLOv2)
Cite this article:
Feng Y, Zhang X, Feng S, et al. Improved SOLOv2 detection method for shield tunnel lining water leakages. Journal of Intelligent Construction, 2023, 1(1): 9180004. https://doi.org/10.26599/JIC.2023.9180004
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Abstract Full text About this article

With the rapid development of urban metros, the detection of shield tunnel leakages has become an important research topic. Progressive technological innovations such as deep learning-based methods provide an effective way to detect tunnel leakages accurately and automatically. However, due to the complex shapes and sizes of leakages, it is challenging for existing algorithms to detect such defects. To address these issues, this paper proposes a novel deep learning-based model named segmenting objects by locations network v2 for tunnel leakages (SOLOv2-TL), which is enhanced by ResNeXt-50, deformable convolution, and path augmentation feature pyramid network (PAFPN). In the SOLOv2-TL, ResNeXt-50 coupled with deformable convolution is the backbone for boosting feature extraction ability that would enable the model sensitivity to leakages of different shapes. The PAFPN is introduced as the neck to reduce the loss of leakage information and more accurately assign leakages of different sizes to their corresponding feature levels. The superior performances of ResNeXt-50 with deformable convolution and PAFPN were validated by ablation tests. Moreover, the segmentation results obtained by SOLOv2-TL were compared with those by the mask region-based convolutional neural network (Mask R-CNN), Cascade Mask R-CNN, and SOLO which demonstrated that the mAP, mAP50, and mAP75 of SOLOv2-TL are higher than those of the other methods, where mAP indicates the mean mask average precision (AP) at intersection over union (IoU) = 0.50:0.05:0.95, mAP50 refers to the mean mask AP with an IoU threshold of 0.50, and mAP75 denotes the mean mask AP with an IoU threshold of 0.75. Finally, a leakage area quantification method is presented.


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About this article

Improved SOLOv2 detection method for shield tunnel lining water leakages

Show Author's information Yong Feng1, Xiaolei Zhang2, Shijin Feng1,2( ), Hongxin Chen2, Yong Zhao2, Yihan Chen1
Urban Mobility Institute, College of Transportation Engineering, Tongji University, Shanghai 201804, China
Key Laboratory of Geotechnical and Underground Engineering of Ministry of Education, Department of Geotechnical Engineering College of Civil Engineering, Tongji University, Shanghai 200092, China

Abstract

With the rapid development of urban metros, the detection of shield tunnel leakages has become an important research topic. Progressive technological innovations such as deep learning-based methods provide an effective way to detect tunnel leakages accurately and automatically. However, due to the complex shapes and sizes of leakages, it is challenging for existing algorithms to detect such defects. To address these issues, this paper proposes a novel deep learning-based model named segmenting objects by locations network v2 for tunnel leakages (SOLOv2-TL), which is enhanced by ResNeXt-50, deformable convolution, and path augmentation feature pyramid network (PAFPN). In the SOLOv2-TL, ResNeXt-50 coupled with deformable convolution is the backbone for boosting feature extraction ability that would enable the model sensitivity to leakages of different shapes. The PAFPN is introduced as the neck to reduce the loss of leakage information and more accurately assign leakages of different sizes to their corresponding feature levels. The superior performances of ResNeXt-50 with deformable convolution and PAFPN were validated by ablation tests. Moreover, the segmentation results obtained by SOLOv2-TL were compared with those by the mask region-based convolutional neural network (Mask R-CNN), Cascade Mask R-CNN, and SOLO which demonstrated that the mAP, mAP50, and mAP75 of SOLOv2-TL are higher than those of the other methods, where mAP indicates the mean mask average precision (AP) at intersection over union (IoU) = 0.50:0.05:0.95, mAP50 refers to the mean mask AP with an IoU threshold of 0.50, and mAP75 denotes the mean mask AP with an IoU threshold of 0.75. Finally, a leakage area quantification method is presented.

Keywords: metro shield tunnel, leakage, instance segmentation, segmenting objects by locations network v2 (SOLOv2)

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Publication history

Received: 10 January 2023
Revised: 23 February 2023
Accepted: 24 February 2023
Published: 12 April 2023
Issue date: March 2023

Copyright

© The Author(s) 2023. Published by Tsinghua University Press.

Acknowledgements

Much of the work described in this paper was supported by the National Key R&D Program of China (No. 2020YFC1808105), Science and Technology Commission of Shanghai Municipality (No. 21DZ1204400), and the National Natural Science Foundation of China (No. 42007250). The authors would like to greatly acknowledge all these financial support and express most sincere gratitude.

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