Abstract
In this work, it is examined the 2D recognition and 3D modelling of concrete tunnel cracks, through visual cues. At the time being, the structural integrity inspection of large-scale infrastructures is mainly performed through visual observations by human inspectors, who identify structural defects, rate them and, then, categorize their severity. The described approach targets at minimum human intervention, for autonomous inspection of civil infrastructures. The shortfalls of existing approaches in crack assessment are being addressed by proposing a novel detection scheme. Although efforts have been made in the field, synergies among proposed techniques are still missing. The holistic approach of this paper exploits the state of the art techniques of pattern recognition and stereo-matching, in order to build accurate 3D crack models. The innovation lies in the hybrid approach for the CNN detector initialization, and the use of the modified census transformation for stereo matching along with a binary fusion of two state-of-the-art optimization schemes. The described approach manages to deal with images of harsh radiometry, along with severe radiometric differences in the stereo pair. The effectiveness of this workflow is evaluated on a real dataset gathered in highway and railway tunnels. What is promising is that the computer vision workflow described in this work can be transferred, with adaptations of course, to other infrastructure such as pipelines, bridges and large industrial facilities that are in the need of continuous state assessment during their operational life cycle.
Highlights
The structural integrity inspection of large-scale infrastructures is mainly performed through visual observations by human inspectors, who identify structural defects, rate them and, categorize their severity
The confusion table is formed, which is a 2 × 2 matrix that reports the number of false positives (FP), false negatives (FN), true positives (TP), and true negatives (TN)
The proposed method have been compared against other techniques like classification trees, k-nearest neighborhood, kNN using adaboost (AB kNN), feed forward neural networks (FNN), Support Vector Machines (SVMs) using different kernels, harmonic separation schemes, low density separation (LDS) and anchor graphs
Summary
The structural integrity inspection of large-scale infrastructures is mainly performed through visual observations by human inspectors, who identify structural defects, rate them and, categorize their severity. Approaches that utilize automated procedures for VI of concrete infrastructures aim to the detection of defects and structure evaluation, often in the content of BIM (building information modelling). Towards this direction, such methods exploit image processing and machine learning techniques. Not much have been done on infrastructure specific, low cost techniques In this contribution, the shortfalls of existing approaches in crack assessment are being addressed by proposing a novel detection scheme. The described approach manages to deal with images of harsh radiometry, along with severe radiometric differences in the stereo pair The effectiveness of this workflow is evaluated on a real dataset gathered in highway and railway tunnels. The rest of the sections are as follows: the overall methodology (i.e. defect detection and 3D modelling) for inspecting civil infrastructures is analysed in Section 2; Section 3 describes the evaluation scheme and proves the effectiveness of the scheme; Section 4 concludes this work with some remarks and future work
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