Cost-effective system for detection and quantification of concrete surface cracks by combination of convolutional neural network and image processing techniques

Miao, Pengyong; Srimahachota, Teeranai

doi:10.1016/j.conbuildmat.2021.123549

Cited by 34 publications

(6 citation statements)

References 48 publications

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“…Dais, et al [30] firstly applied deep learning techniques on masonry images with pixel-level segmentation. Miao and Srimahachota [31] com-bined a trained CNN and an image processing method to detect and quantify cracks in a semi-automatic way. Fu, Meng, Li and Wang [6] proposed an algorithm based on Dense-DeepLabv3+ network to segment bridge crack images.…”

Section: Defect Detection Based On Machine Learningmentioning

confidence: 99%

Quantification of Structural Defects Using Pixel Level Spatial Information from Photogrammetry

Guo

Shen

Linke³

et al. 2023

Sensors

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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.

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Section: Defect Detection Based On Machine Learningmentioning

confidence: 99%

Quantification of Structural Defects Using Pixel Level Spatial Information from Photogrammetry

Guo

Shen

Linke³

et al. 2023

Sensors

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“…Representative research in this field can be found in [3][4][5][6][7][8][9][10][11][12][13]. However, as a deep learning method, CNN-based segmentation suffers from the preparation of pixel-level labels, which is known to be rather costly [14].…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Approach for Concrete Crack Segmentation Based on Deformable Oriented-YOLOv4 and Image Processing Techniques

He,

Su,

Deng

2024

Applied Sciences

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Regular crack inspection plays a significant role in the maintenance of concrete structures. However, most deep-learning-based methods suffer from the heavy workload of pixel-level labeling and the poor performance of crack segmentation with the presence of background interferences. To address these problems, the Deformable Oriented YOLOv4 (DO-YOLOv4) is first developed for crack detection based on the traditional YOLOv4, in which crack features can be effectively extracted by deformable convolutional layers, and the crack regions can be tightly enclosed by a series of oriented bounding boxes. Then, the proposed DO-YOLOv4 is further utilized in combination with the image processing techniques (IPTs), leading to a novel hybrid approach, termed DO-YOLOv4-IPTs, for crack segmentation. The experimental results show that, owing to the high precision of DO-YOLOv4 for crack detection under background noise, the present hybrid approach DO-YOLOv4-IPTs outperforms the widely used Convolutional Neural Network (CNN)-based crack segmentation methods with less labeling work and superior segmentation accuracy.

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“…In this regard, crack is a commonly used indicator to evaluate deterioration significance of structures. [1][2][3] As such, crack detection and classification is of great importance to support prediction of residual lifetime and avoid structural failure. Traditional manual inspections that currently used in practice have the drawbacks of time consuming and labour intensive, and is heavily dependent on the experience of operators.…”

Section: Introductionmentioning

confidence: 99%

Crack detection of concrete structures using deep convolutional neural networks optimized by enhanced chicken swarm algorithm

Rashidi

Samali

et al. 2022

Structural Health Monitoring

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With the rapid increase of ageing infrastructures worldwide, effective and robust inspection techniques are highly demanding to evaluate structural conditions and residual lifetime. The damages on structural surfaces, for example, spalling, crack, rebar buckling and exposure, are important indicators to assess the structural condition. In fact, several state-of-the-art automated inspection techniques using these indicators have been developed to reduce human-conducted onsite inspection activities. However, the efficiency of these techniques is still required to be improved in terms of accuracy and computational cost. In this study, a vision-based crack diagnosis method is developed using deep convolutional neural network (DCNN) and enhanced chicken swarm algorithm (ECSA). A DCNN model is designed with a deep architecture, consisting of six convolutional layers, two pooling layers and three fully connected layers. To enhance the generalisation capacity of trained model, ECSA is introduced to optimize meta-parameters of the DCNN model. The model is trained and tested using image patches cropped from raw images obtained from damaged concrete samples. Finally, a comparative study on different crack detection techniques is conducted to evaluate performance of the proposed method via a group of statistical evaluation indicators.

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Cost-effective system for detection and quantification of concrete surface cracks by combination of convolutional neural network and image processing techniques

Cited by 34 publications

References 48 publications

Quantification of Structural Defects Using Pixel Level Spatial Information from Photogrammetry

Quantification of Structural Defects Using Pixel Level Spatial Information from Photogrammetry

A Novel Hybrid Approach for Concrete Crack Segmentation Based on Deformable Oriented-YOLOv4 and Image Processing Techniques

Crack detection of concrete structures using deep convolutional neural networks optimized by enhanced chicken swarm algorithm

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