The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B4-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2020, 741–745, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-741-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2020, 741–745, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-741-2020

  25 Aug 2020

25 Aug 2020

TACK PROJECT: TUNNEL AND BRIDGE AUTOMATIC CRACK MONITORING USING DEEP LEARNING AND PHOTOGRAMMETRY

V. Belloni1, A. Sjölander2, R. Ravanelli1, M. Crespi1, and A. Nascetti3 V. Belloni et al.
  • 1Geodesy and Geomatics Division (DICEA), Sapienza University of Rome, Rome, Italy
  • 2Division of Concrete Structures, KTH - Royal Institute of Technology, Stockholm, Sweden
  • 3Geoinformatics Division, KTH - Royal Institute of Technology, Stockholm, Sweden

Keywords: Infrastructure monitoring, Crack detection, Photogrammetry, Deep learning

Abstract. Civil infrastructures, such as tunnels and bridges, are directly related to the overall economic and demographic growth of countries. The aging of these infrastructures increases the probability of catastrophic failures that results in loss of lives and high repair costs; all over the world, these factors drive the need for advanced infrastructure monitoring systems. For these reasons, in the last years, different types of devices and innovative infrastructure monitoring techniques have been investigated to automate the process and overcome the main limitation of standard visual inspections that are used nowadays. This paper presents some preliminary findings of an ongoing research project, named TACK, that combines advanced deep learning techniques and innovative photogrammetric algorithms to develop a monitoring system. Specifically, the project focuses on the development of an automatic procedure for crack detection and measurement using images of tunnels and bridges acquired with a mobile mapping system. In this paper, some preliminary results are shown to investigate the potential of a deep learning algorithm in detecting cracks occurred in concrete material. The model is a CNN (Convolutional Neural Network) based on the U-Net architecture; in this study, we tested the transferability of the model that has been trained on a small available labeled dataset and tested on a large set of images acquired using a customized mobile mapping system. The results have shown that it is possible to effectively detect cracks in unseen imagery and that the primary source of errors is the false positive detection of crack-like objects (i.e., contact wires, cables and tile borders).