Skip to main content
SpringerLink
Log in

AI-enabled Underground Water Pipe non -destructive Inspection

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

A study conducted by the World Bank indicated that the global annual economic losses from the water leakage are estimated at US$ 14.6 billion. For this reason, locating and repairing water leaks as well as the maintenance of water pipelines is extremely important for the optimization and rationalization of water resources. The basic technique for inspecting water delivery infrastructure is the water audit but this technique does not provide any information about the location of the water leakage. This paper focuses on this gap, aiming to provide information not only for the location of the water leakage but also for the level of water pipe material degradation due to its corrosion before the leakage presents. Here, the identification of the extent and severity of the evolving defect of water pipes is performed through deep learning models using simulated and real Ground Penetrating Radar (GPR) data. Synthetic GPR images are generated, with underground water pipes that either present leakage or no in various steps of their corrosion, using gprMax software. Especially, this addresses as a solution YOLOv5 algorithm for the automatic detection of water pipes and leaks in the underground space and a conditional Generative Adversarial Network (cGAN) for the investigation of water pipe material degradation. The results reveal that the YOLOv5 algorithm distinguishes the regions of pipes in GPR data and classified correctly the pipes which present leakage or no, and they are better than the corresponding results of other literature baseline methods. In addition, as shown through extensive simulations on generated GPR data the proposed cGAN produces high quality results that contribute to revelation of the extent and severity of the evolving defect of pipeline due to its corrosion.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Data Availibility Statement

The referred data will be available on request.

References

  1. Ahmed M, Hashmi KA, Pagani A, Liwicki M, Stricker D, Afzal MZ (2021) Survey and performance analysis of deep learning based object detection in challenging environments. Sensors 21(15):5116

    Article  Google Scholar 

  2. Ruchti GF (2017) Water Pipeline Condition Assessment, 1st edn. Am Soc Civ Eng (ASCE), Virginia, United States

  3. Alshamy HM, Sadah JWA, Saeed TR, Mohammed SA, Hatem GM, Gatan AH (2021) Evaluation of gpr detection for buried objects material with different depths and scanning angles. In IOP Conference Series: Mater Sci Eng, vol 1090, p 012042, IOP Publishing

  4. Ayala-Cabrera D, Campbell E, Carreño-Alvarado E, Izquierdo J, Pérez-García R (2014) Water leakage evolution based on gpr interpretations. Procedia Engineering 89:304–310

    Article  Google Scholar 

  5. Pham MT, Lefèvre S (2018) Buried object detection from b-scan ground penetrating radar data using faster-rcnn. In: IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, pp 6804–6807, IEEE

  6. Besaw LE, Stimac PJ (2015) Deep convolutional neural networks for classifying gpr b-scans. In Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XX, vol 9454, pp 385–394 . SPIE

  7. Wentai L, Zeng S, Zhao J (2013) An improved back projection imaging algorithm for subsurface target detection. Turk J Electr Eng Comput Sci 21(6):1820–1826

  8. Dinh K, Gucunski N, Duong T (2018) An algorithm for automatic localization and detection of rebars from gpr data of concrete bridge decks. Autom Constr 89, p 292–298

  9. Chen Y, Han C, Li Y, Huang Z, Jiang Y, Wang N, Zhang Z (2019) Simpledet: A simple and versatile distributed framework for object detection and instance recognition. J Mach Learn Res 20(156):1–8

    MathSciNet  Google Scholar 

  10. Skartados E, Kostavelis I, Giakoumis D, Tzovaras D (2019) Hybrid geometric similarity and local consistency measure for gpr hyperbola detection. In International Conference on Computer Vision Systems, pp 224–233 . Springer

  11. Skartados E, Kargakos A, Tsiogas E, Kostavelis I, Giakoumis D, Tzovaras D (2019) Gpr antenna localization based on a-scans. In 2019 27th European Signal Processing Conference (EUSIPCO), pp 1–5, IEEE

  12. Kouros G, Kotavelis I, Skartados E, Giakoumis D, Tzovaras D, Simi A, Manacorda G (2018) 3d underground mapping with a mobile robot and a gpr antenna. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3218–3224, IEEE

  13. Qiu Z, Zhao Z, Chen S, Zeng J, Huang Y, Xiang B (2022) Application of an improved yolov5 algorithm in real-time detection of foreign objects by ground penetrating radar. Remote sensing

  14. Almahasneh M, Paiement A, Xie X, Aboudarham J (2022) Mlmt-cnn for object detection and segmentation in multi-layer and multi-spectral images. Machine vision and applications 33(9)

  15. Ding J, Chen B, Liu H, Huang M (2016) Convolutional neural network with data augmentation for sar target recognition. IEEE Geoscience and remote sensing letters 13(3):364–368

    Google Scholar 

  16. Dinh K, Gucunski N, Duong T (2018) An algorithm for automatic localization and detection of rebars from gpr data of concrete bridge decks. Autom Constr 89:292–298

    Article  Google Scholar 

  17. Tong Z, Yuan D, Gao J, Wang Z (2020) Pavement defect detection with fully convolutional network and an uncertainty framework. Computer-aided civil and infrastructure engineering 35, 832–849

  18. Ahmed M, Hashmi KA, Pagani A, Liwicki M, Stricker D, Afzal MZ (2021) Survey and performance analysis of deep learning based object detection in challenging environments. Sensors 21(15):5116

  19. Zaidi SSA, Ansari MS, Aslam A, Kanwal N, Asghar M, Lee B (2022) A survey of modern deep learning based object detection models. Digital Signal Processing, 103514

  20. Lin T, Wang Y, Liu X, Qiu X (2021) A survey of transformers. arXiv preprint arXiv:2106.04554

  21. Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 580–587

  22. Girshick R (2015) Fast r-cnn. In Proceedings of the IEEE International Conference on Computer Vision, pp 1440–1448

  23. He K, Zhang X, Ren S, Sun J (2015) Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE transactions on pattern analysis and machine intelligence 37(9):1904–1916

    Article  Google Scholar 

  24. He K, Gkioxari G, Dollár P., Girshick, R (2017) Mask r-cnn. In: Proceedings of the IEEE International conference on computer vision, pp 2961–2969

  25. Cai Z, Vasconcelos N.: Cascade r-cnn: Delving into high quality object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6154–6162

  26. Li Y, Chen Y, Wang N, Zhang Z (2019) Scale-aware trident networks for object detection, 10 arXiv:1901.01892v2

  27. Sun P, Zhang R, Jiang Y, Kong T, Xu C, Zhan W, Tomizuka M, Li L, Yuan Z, Wang C, et al (2021) Sparse r-cnn: End-to-end object detection with learnable proposals. In proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 14454–14463

  28. Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: Unified, real-time object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 779–788

  29. Karras T, Aila T, Laine S, Lehtinen J (2018) Progressive growing of gans for improved quality, stability, and variation

  30. Kaur P, Dana KJ, Romero FA, Gucunski N (2015) Automated gpr rebar analysis for robotic bridge deck evaluation. IEEE transactions on cybernetics 46(10):2265–2276

    Article  Google Scholar 

  31. Jocher G, Chaurasia A, Stoken A, Borovec J, NanoCode012, Kwon Y, TaoXie, Fang J, imyhxy, Michael K, Lorna, VA, Montes D, Nadar J, Laughing, tkianai, yxNONG, Skalski P, Wang Z, Hogan A, Fati C, Mammana L, AlexWang1900, Patel D, Yiwei D, You F, Hajek J, Diaconu L, Minh MT ultralytics/yolov5: V6.1 - TensorRT, TensorFlow Edge TPU and OpenVINO Export and Inference, https://doi.org/10.5281/zenodo.6222936

  32. Wang CY, Bochkovskiy A, Liao HYM (2022) Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv preprint arXiv:2207.02696

  33. Chen K, Li J, Lin W, See J, Wang J, Duan L, Chen Z, He C, Zou J (2019) Towards accurate one-stage object detection with ap-loss. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 5119–5127

  34. Krizhevsky A, Sutskever I, Hinton GE (2017) Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems 60(6):84–90

    Google Scholar 

  35. Lee J, Lee YJ, Shim CS (2020) Probabilistic prediction of mechanical characteristics of corroded strands. Engineering Structures 203:109882

    Article  Google Scholar 

  36. Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In Proceedings of the IEEE International Conference on Computer Vision, pp 2980–2988

  37. Kim K, Lee HS (2020) Probabilistic anchor assignment with iou prediction for object detection. In European Conference on Computer Vision, pp 355–371, Springer

  38. Tan M, Pang R, Le QV (2020) Efficientdet: Scalable and efficient object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 10781–10790

  39. Tan M, Le Q (2019) Efficientnet: Rethinking model scaling for convolutional neural networks. In International Conference on Machine Learning, pp 6105–6114, PMLR

  40. Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, et al (2015) Imagenet large scale visual recognition challenge. Int J Comput Vis 115(3):211–252

  41. Shinya Y (2021) USB: Universal-scale object detection benchmark. arXiv:2103.14027

  42. Zhang S, Chi C, Yao Y, Lei Z, Li SZ (2020) Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 9759–9768

  43. Singh B, Davis LS (2018) An analysis of scale invariance in object detection snip. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 3578–3587

  44. Wang X, Zhang S, Yu Z, Feng L, Zhang W (2020) Scale-equalizing pyramid convolution for object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 13359–13368

  45. Beal J, Kim E, Tzeng E, Park DH, Zhai A, Kislyuk D (2020) Toward transformer-based object detection. arXiv preprint arXiv:2012.09958

  46. Navarro P, Cintas C, Lucena M, Fuertes JM, Segura R, Delrieux C, González-José R (2022) Reconstruction of iberian ceramic potteries using generative adversarial networks. Scientific reports 12(1):1–11

    Article  Google Scholar 

  47. Zhu X, Su W, Lu L, Li B, Wang X, Dai J (2020) Deformable detr: Deformable transformers for end-to-end object detection. arXiv preprint arXiv:2010.04159

  48. Papadopoulos S, Dimitriou N, Drosou A, Tzovaras D (2021) Modelling spatio-temporal ageing phenomena with deep generative adversarial networks. Signal processing Image Commun 94:116200

    Article  Google Scholar 

  49. Zhang H, Li F, Liu S, Zhang L, Su H, Zhu J, Ni LM, Shum HY (2022) Dino: Detr with improved denoising anchor boxes for end-to-end object detection. arXiv preprint arXiv:2203.03605

  50. Liu S, Li F, Zhang H, Yang X, Qi X, Su H, Zhu J, Zhang L (2022) Dab-detr: Dynamic anchor boxes are better queries for detr. arXiv preprint arXiv:2201.12329

  51. Li F, Zhang H, Liu S, Guo J, Ni LM, Zhang L (2022) Dn-detr: Accelerate detr training by introducing query denoising. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 13619–13627

  52. Gupta A, Narayan S, Joseph K, Khan S, Khan FS, Shah M (2022) Ow-detr: Open-world detection transformer. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 9235–9244

  53. Chi C, Wei F, Hu H (2020) Relationnet\(++\): Bridging visual representations for object detection via transformer decoder. In NeurIPS

  54. Lee J, Lee YJ, Shim CS (2020) Probabilistic prediction of mechanical characteristics of corroded strands. Engineering Structures 203, 109882

  55. Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M et al (2015) Imagenet large scale visual recognition challenge. Int J Comput Vis 115(3):211–252

    Article  MathSciNet  Google Scholar 

  56. Yue Y, Liu H, Meng X, Li Y, Du Y (2021) Generation of high-precision ground penetrating radar images using improved least square generative adversarial networks. Remote Sensing 13(22):4590

  57. Zhang X, Han L, Robinson M, Gallagher A (2021) A gans-based deep learning framework for automatic subsurface object recognition from ground penetrating radar data. IEEE Access 9, 39009–39018

  58. Veal C, Dowdy J, Brockner B, Anderson DT, Ball JE, Scott G (2018) Generative adversarial networks for ground penetrating radar in hand held explosive hazard detection. International Society for Optics and Photonics 10628, 18

  59. Pantraki E, Kotropoulos C (2021) Face aging using global and pyramid generative adversarial networks. Mach Vis Appl 32(82)

  60. Truong T, Yanushkevich S (2019) Generative adversarial network for radar signal synthesis. In 2019 International Joint Conference on Neural Networks (IJCNN), pp 1–7, IEEE

  61. Karras T, Aila T, Laine S, Lehtinen J (2018) Progressive growing of gans for improved quality, stability, and variation. In International Conference on Learning Representations

  62. Mathieu M, Couprie C, LeCun Y (2015) Deep multi-scale video prediction beyond mean square error. arXiv preprint arXiv:1511.05440

  63. Liu MY, Breuel T, Kautz J (2017) Unsupervised image-to-image translation networks. Advances in neural information processing systems 30

  64. Zhu JY, Park T, Isola AAP, Efros (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks, vol 1 The IEEE International Conference on Computer Vision (ICCV)

  65. Tong Z, Yuan D, Gao J, Wang Z (2020) Pavement defect detection with fully convolutional network and an uncertainty framework. Computer-aided civil and infrastructure engineering 35:832–849

    Article  Google Scholar 

  66. Salimans T, Goodfellow I, Zaremba W, Cheung V, Radford A, Chen X (2016) Improved techniques for training gans, 2234–2242 arXiv:1606.03498v

  67. Veal C, Dowdy J, Brockner B, Anderson DT, Ball JE, Scott G (2018) Generative adversarial networks for ground penetrating radar in hand held explosive hazard detection. International Society for Optics and Photonics 10628:18

    Google Scholar 

  68. Li J, Liang X, Wei Y, Xu T, Feng J, Yan S (2017) Perceptual generative adversarial networks for small object detection. In Proceedings of the IEEE Conference on ComputerVision and Pattern Recognition, pp 1222–1230

  69. Kim D, Park S, Kang D, Paik J (2019) Improved center and scale prediction-based pedestrian detection using convolutional block. In 2019 IEEE 9th International Conference on Consumer Electronics (ICCE-Berlin), pp 418–419, IEEE

  70. He K, Zhang X, Ren S, Sun J (2015) Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE transactions on pattern analysis and machine intelligence 37(9):1904–1916

  71. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing 13(4):600–612

    Article  Google Scholar 

  72. Liu S, Qi L, Qin H, Shi J, Jia J (2018) Path aggregation network for instance segmentation. In proceedings of the IEEE conference on computer vision and pattern recognition, pp 8759–8768

  73. Warren C, Giannopoulos A, Giannakis I (2016) gprmax: Open source software to simulate electromagnetic wave propagation for ground penetrating radar. Comput Phys Commun 46(10):163–170

    Article  Google Scholar 

  74. Wentai L, Zeng S, Zhao J (2013) An improved back projection imaging algorithm for subsurface target detection. Turk J Electr Eng Comput Sci 21(6):1820–1826

    Google Scholar 

  75. Johnson J, Alahi A, Fei-Fei L (2016) Perceptual losses for real-time style transfer and super-resolution. In european conference on computer vision, pp 694–711 Springer

  76. Yue Y, Liu H, Meng X, Li Y, Du Y (2021) Generation of high-precision ground penetrating radar images using improved least square generative adversarial networks. Remote Sensing 13(22):4590

    Article  Google Scholar 

  77. Warren C, Giannopoulos A, Giannakis I (2016) gprmax: Open source software to simulate electromagnetic wave propagation for ground penetrating radar. Comput Phys Commun46(10):163–170

  78. Zhang X, Han L, Robinson M, Gallagher A (2021) A gans-based deep learning framework for automatic subsurface object recognition from ground penetrating radar data. IEEE Access 9:39009–39018

    Article  Google Scholar 

  79. Navarro P, Cintas C, Lucena M, Fuertes JM, Segura R, Delrieux C, González-José R (2022) Reconstruction of iberian ceramic potteries using generative adversarial networks. Scientific reports 12(1):1–11

  80. Wu Y, Kirillov A, Massa F, Lo WY, Girshick R (2019) Detectron2. https://github.com/facebookresearch/detectron2

  81. Chen K, Wang J, Pang J, Cao Y, Xiong Y, Li X, Sun S, Feng W, Liu Z, Xu J, Zhang Z, Cheng D, Zhu C, Cheng T, Zhao Q, Li B, Lu X, Zhu R, Wu Y, Dai J, Wang J, Shi J, Ouyang W, Loy CC, Lin D (2019) MMDetection: Open mmlab detection toolbox and benchmark. arXiv preprint arXiv:1906.07155

  82. Chen Y, Han C, Li Y, Huang Z, Jiang Y, Wang N, Zhang Z (2019) Simpledet: A simple and versatile distributed framework for object detection and instance recognition. J Mach Learn Res, 20(156):1–8

  83. Heusel M, Ramsauer H, Unterthiner T, Nessler B, Hochreiter S (2017) Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems 30

  84. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing 13(4):600–612

Download references

Acknowledgements

This work was supported by the TERRAPIN project that it has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 824990 and by the PALIMPSISTO project co-financed from the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation B’ phase, under the call RESEARCH-CREATE-INNOVATE (project code:T2EDK-01894).

Author information

Authors and Affiliations

  1. Information Technologies Institute, Centre for Research and Technology Hellas, 6th km Charilaou-Thermi Rd, 57001, Thessaloniki, Greece

    Georgios-Fotios Angelis, Dimitrios Chorozoglou, Stavros Papadopoulos, Anastasios Drosou, Dimitrios Giakoumis & Dimitrios Tzovaras

Authors
  1. Georgios-Fotios Angelis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dimitrios Chorozoglou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Stavros Papadopoulos
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Anastasios Drosou
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Dimitrios Giakoumis
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Dimitrios Tzovaras
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Georgios-Fotios Angelis or Dimitrios Chorozoglou.

Ethics declarations

Conflict of interests and funding

All authors certify that they have no affiliation or involvement with any organization or entity with financial or non-financial interests in the subject matter or material covered by this manuscript.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Papadopoulos, Drosou, Giakoumis and Tzovaras These authors contributed equally to this work.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Angelis, GF., Chorozoglou, D., Papadopoulos, S. et al. AI-enabled Underground Water Pipe non -destructive Inspection. Multimed Tools Appl 83, 18309–18332 (2024). https://doi.org/10.1007/s11042-023-15797-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-15797-w

Keywords

Search

Navigation