Skip to main content
Erschienen in:
Buchtitelbild

2019 | OriginalPaper | Buchkapitel

Road Damage Detection Acquisition System Based on Deep Neural Networks for Physical Asset Management

verfasst von : Andres Angulo, Juan Antonio Vega-Fernández, Lina Maria Aguilar-Lobo, Shailendra Natraj, Gilberto Ochoa-Ruiz

Erschienen in: Advances in Soft Computing

Verlag: Springer International Publishing

Aktivieren Sie unsere intelligente Suche um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Research on damage detection of road surfaces has been an active area of research, but most studies have focused so far on the detection of the presence of damages. However, in real-world scenarios, road managers need to clearly understand the type of damage and its extent in order to take effective action in advance or to allocate the necessary resources. Moreover, currently there are few uniform and openly available road damage datasets, leading to a lack of a common benchmark for road damage detection. Such dataset could be used in a great variety of applications; herein, it is intended to serve as the acquisition component of a physical asset management tool which can aid governments agencies for planning purposes, or by infrastructure maintenance companies. In this paper, we make two contributions to address these issues. First, we present a large-scale road damage dataset, which includes a more balanced and representative set of damages. This dataset is composed of 18,034 road damage images captured with a smartphone, with 45,435 instances road surface damages. Second, we trained different types of object detection methods, both traditional (an LBP-cascaded classifier) and deep learning-based, specifically, MobileNet and RetinaNet, which are amenable for embedded and mobile and We compare the accuracy and inference time of all these models with others in the state of the art.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literatur
1.
Zurück zum Zitat Koch, C., Asce, A.M., Jog, G.M., Brilakis, I.: Automated pothole distress assessment using asphalt pavement video data. J. Comput. Civ. Eng. 27, 4 (2013)CrossRef Koch, C., Asce, A.M., Jog, G.M., Brilakis, I.: Automated pothole distress assessment using asphalt pavement video data. J. Comput. Civ. Eng. 27, 4 (2013)CrossRef
2.
Zurück zum Zitat Oliveira, H., Correia, P.L.: Automatic road crack detection and characterization. IEEE Trans. Intell. Transp. Syst. 14(1), 155–168 (2013)CrossRef Oliveira, H., Correia, P.L.: Automatic road crack detection and characterization. IEEE Trans. Intell. Transp. Syst. 14(1), 155–168 (2013)CrossRef
3.
Zurück zum Zitat Radopoulou, S.C., Bralakis, I.: Patch detection for pavement assessment. Autom. Constr. 53, 95–104 (2015)CrossRef Radopoulou, S.C., Bralakis, I.: Patch detection for pavement assessment. Autom. Constr. 53, 95–104 (2015)CrossRef
4.
Zurück zum Zitat Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: Vision meets robotics: the KITTI dataset. Int. J. Robot. Res. 32(11), 1231–1237 (2013)CrossRef Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: Vision meets robotics: the KITTI dataset. Int. J. Robot. Res. 32(11), 1231–1237 (2013)CrossRef
5.
Zurück zum Zitat Medina, R., Llamas, J., Zalama E., Gómez-García-Bermejo, J.: Enhanced automatic detection of road surface cracks by combining 2D/3D image processing techniques. In: IEEE International Conference on Image Processing (ICIP), Paris, pp. 778–782 (2014) Medina, R., Llamas, J., Zalama E., Gómez-García-Bermejo, J.: Enhanced automatic detection of road surface cracks by combining 2D/3D image processing techniques. In: IEEE International Conference on Image Processing (ICIP), Paris, pp. 778–782 (2014)
6.
Zurück zum Zitat Ryu, S.K., Kim, T., Kim, Y.R.: Image-based pothole detection system for ITS service and road management system. Math. Probl. Eng. 2015, 10 (2015)CrossRef Ryu, S.K., Kim, T., Kim, Y.R.: Image-based pothole detection system for ITS service and road management system. Math. Probl. Eng. 2015, 10 (2015)CrossRef
7.
Zurück zum Zitat Mathavan, S., Kamal, K., Rahman, M.: A review of three-dimensional imaging technologies for pavement distress detection and measurements. IEEE Trans. Intell. Transp. Syst. 16(5), 2353–2362 (2015)CrossRef Mathavan, S., Kamal, K., Rahman, M.: A review of three-dimensional imaging technologies for pavement distress detection and measurements. IEEE Trans. Intell. Transp. Syst. 16(5), 2353–2362 (2015)CrossRef
8.
Zurück zum Zitat Schnebele, E., Tanyu, B.F., Cervone, F., Waters, G.: Review of remote sensing methodologies for pavement management and assessment. Eur. Transp. Res. Rev. 7, 7 (2015)CrossRef Schnebele, E., Tanyu, B.F., Cervone, F., Waters, G.: Review of remote sensing methodologies for pavement management and assessment. Eur. Transp. Res. Rev. 7, 7 (2015)CrossRef
9.
Zurück zum Zitat Koch, C., Giorgieva, K., Kasireddy, V., Akinci, B., Fieguth, P.: A review of computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure. Adv. Eng. Inform. 29, 196–210 (2015)CrossRef Koch, C., Giorgieva, K., Kasireddy, V., Akinci, B., Fieguth, P.: A review of computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure. Adv. Eng. Inform. 29, 196–210 (2015)CrossRef
10.
Zurück zum Zitat Mohan, A., Poobal, S.: Crack detection using image processing: a critical review and analysis. Alexandria Eng. J. 57(2), 787–798 (2018)CrossRef Mohan, A., Poobal, S.: Crack detection using image processing: a critical review and analysis. Alexandria Eng. J. 57(2), 787–798 (2018)CrossRef
11.
Zurück zum Zitat Hoang, N.D., Nguyen, Q.L.: A novel method for asphalt pavement crack classification based on image processing and machine learning. Eng. Comput. 35(2), 487–498 (2018)MathSciNetCrossRef Hoang, N.D., Nguyen, Q.L.: A novel method for asphalt pavement crack classification based on image processing and machine learning. Eng. Comput. 35(2), 487–498 (2018)MathSciNetCrossRef
12.
Zurück zum Zitat Hoang, N.D.: An artificial intelligence method for asphalt pavement pothole detection using least squares support vector machine and neural network with steerable filter-based feature extraction. Hindawi Adv. Civ. Eng. 2018, 12 (2018) Hoang, N.D.: An artificial intelligence method for asphalt pavement pothole detection using least squares support vector machine and neural network with steerable filter-based feature extraction. Hindawi Adv. Civ. Eng. 2018, 12 (2018)
13.
Zurück zum Zitat Cha, Y.J., Choi, W., Büyüköztürk, O.: Deep learning-based crack damage detection using convolutional neural network. Comput.-Aided Civ. Infrastruct. Eng. 32(5), 361–378 (2017)CrossRef Cha, Y.J., Choi, W., Büyüköztürk, O.: Deep learning-based crack damage detection using convolutional neural network. Comput.-Aided Civ. Infrastruct. Eng. 32(5), 361–378 (2017)CrossRef
14.
Zurück zum Zitat Tedeschi, A., Benedetto, F.: A real time pavement crack and pothole recognition system for mobile Android-based devices. AEI 32, 11–25 (2017) Tedeschi, A., Benedetto, F.: A real time pavement crack and pothole recognition system for mobile Android-based devices. AEI 32, 11–25 (2017)
15.
Zurück zum Zitat Liu, L., Ouyang, W., Wang, X., Fieguth, P., Liu, X., Pietikäinen, M.: Deep learning for generic object detection: a survey. arXiv:1809.02165v2 (2016) Liu, L., Ouyang, W., Wang, X., Fieguth, P., Liu, X., Pietikäinen, M.: Deep learning for generic object detection: a survey. arXiv:​1809.​02165v2 (2016)
17.
Zurück zum Zitat Ale, L., Zhang, N., Li, L.: Road damage detection using RetinaNet. In: International Conference on Big Data 2018, pp. 5197–5200 (2018) Ale, L., Zhang, N., Li, L.: Road damage detection using RetinaNet. In: International Conference on Big Data 2018, pp. 5197–5200 (2018)
18.
Zurück zum Zitat Pereira, V., Tamura, S., Hayamizu, S., Fukain H.: A deep learning-based approach for road pothole detection in Timor Leste. In: 2018 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI), Singapore, pp. 279–284 (2018) Pereira, V., Tamura, S., Hayamizu, S., Fukain H.: A deep learning-based approach for road pothole detection in Timor Leste. In: 2018 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI), Singapore, pp. 279–284 (2018)
19.
Zurück zum Zitat Maeda, H., Sekimoto, Y., Seto, T., Kashiyama, T., Omata, H: Road damage detection using deep neural networks with images captured through a smartphone (2018) Maeda, H., Sekimoto, Y., Seto, T., Kashiyama, T., Omata, H: Road damage detection using deep neural networks with images captured through a smartphone (2018)
Metadaten
Titel
Road Damage Detection Acquisition System Based on Deep Neural Networks for Physical Asset Management
verfasst von
Andres Angulo
Juan Antonio Vega-Fernández
Lina Maria Aguilar-Lobo
Shailendra Natraj
Gilberto Ochoa-Ruiz
Copyright-Jahr
2019
DOI
https://doi.org/10.1007/978-3-030-33749-0_1