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. 2023 Jun 25;23(13):5878.
doi: 10.3390/s23135878.

Quantification of Structural Defects Using Pixel Level Spatial Information from Photogrammetry

Youheng Guo  1   2 , Xuesong Shen  1 , James Linke  2 , Zihao Wang  2 , Khalegh Barati  1
Affiliations

Quantification of Structural Defects Using Pixel Level Spatial Information from Photogrammetry

Youheng Guo et al. Sensors (Basel). .

Abstract

Aging infrastructure has drawn increased attention globally, as its collapse would be destructive economically and socially. Precise quantification of minor defects is essential for identifying issues before structural failure occurs. Most studies measured the dimension of defects at image level, ignoring the third-dimensional information available from close-range photogrammetry. This paper aims to develop an efficient approach to accurately detecting and quantifying minor defects on complicated infrastructures. Pixel sizes of inspection images are estimated using spatial information generated from three-dimensional (3D) point cloud reconstruction. The key contribution of this research is to obtain the actual pixel size within the grided small sections by relating spatial information. To automate the process, deep learning technology is applied to detect and highlight the cracked area at the pixel level. The adopted convolutional neural network (CNN) achieves an F1 score of 0.613 for minor crack extraction. After that, the actual crack dimension can be derived by multiplying the pixel number with the pixel size. Compared with the traditional approach, defects distributed on a complex structure can be estimated with the proposed approach. A pilot case study was conducted on a concrete footpath with cracks distributed on a selected 1500 mm × 1500 mm concrete road section. Overall, 10 out of 88 images are selected for validation; average errors ranging from 0.26 mm to 0.71 mm were achieved for minor cracks under 5 mm, which demonstrates a promising result of the proposed study.

Keywords: convolutional neural network; crack detection; crack measurement; photogrammetry.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The workflow of crack quantification.
Figure 2
Figure 2
The results of different models for obvious and minor cracks: (a) original image of obvious cracks, (b) VGG16 + Focal Loss with more training data sets for obvious cracks, (c) original image of minor cracks, and (d) VGG16 + Focal Loss with more training data sets for minor cracks.
Figure 3
Figure 3
Flowchart of calculating grid pixel size: (a) grid, (b) corner pixel, (c) nearest three tie points on image, (d) pixel perimeter, (e) corresponded three 3D tie points, (f) actual perimeter.
Figure 4
Figure 4
The process of measuring crack pixel dimension: (a) raw image of the crack; (b) labelled crack; (c) and crack mask; (d) automatically counted crack width in pixel; (e) partially zoomed in crack; (f) part of labelled measurement spots on the target crack.
Figure 5
Figure 5
Ten images shot from different angles.
Figure 6
Figure 6
The process of defining 3D scale factor: (a) the referenced length of the crack; (b) the referenced length of the crack.
Figure 7
Figure 7
Calculate the pixel size of corner grid: (a) corner point; (b) nearest three tie points.
Figure 8
Figure 8
(a) Heat map on pixel size of an image; (b) noise of the created point cloud surface.
Figure 9
Figure 9
Automatically counted pixel number.
Figure 10
Figure 10
Some obvious errors caused by the detection algorithm.
See this image and copyright information in PMC

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