A Study on Crack Depth Measurement in Steel Structures Using Image-Based Intensity Differences

Jung, Ju-Yeong; Yoon, Hyuk-Jin; Cho, Hyun-Woo

doi:10.1155/2018/7530943

Cited by 5 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is practically meaningful to measure and detect the depth of the cracks. The detection of crack depth can predict the future trend of the crack, which is helpful to repair the pavement in time and reduce potential safety risks [11]. In recent years, 3D imaging technology has achieved great progress, making cracks detection in 3D images has become a new research direction for scholars.…”

Section: Introductionmentioning

confidence: 99%

Review of Pavement Defect Detection Methods

2020

View full text Add to dashboard Cite

Road pavement cracks detection has been a hot research topic for quite a long time due to the practical importance of crack detection for road maintenance and traffic safety. Many methods have been proposed to solve this problem. This paper reviews the three major types of methods used in road cracks detection: image processing, machine learning and 3D imaging based methods. Image processing algorithms mainly include threshold segmentation, edge detection and region growing methods, which are used to process images and identify crack features. Crack detection based traditional machine learning methods such as neural network and support vector machine still relies on hand-crafted features using image processing techniques. Deep learning methods have fundamentally changed the way of crack detection and greatly improved the detection performance. In this work, we review and compare the deep learning neural networks proposed in crack detection in three ways, classification based, object detection based and segmentation based. We also cover the performance evaluation metrics and the performance of these methods on commonly-used benchmark datasets. With the maturity of 3D technology, crack detection using 3D data is a new line of research and application. We compare the three types of 3D data representations and study the corresponding performance of the deep neural networks for 3D object detection. Traditional and deep learning based crack detection methods using 3D data are also reviewed in detail. INDEX TERMS Crack detection, image processing, deep learning, 3D imaging.

show abstract

Section: Introductionmentioning

confidence: 99%

Review of Pavement Defect Detection Methods

2020

View full text Add to dashboard Cite

show abstract

“…Transforming the pixel intensity level to 16 bits' grey level from the 8 bits as in the OriginLab [30] will enhance the depth resolution of the crack depth. e first approach of estimating the crack depth with use of a camera image was finding the average pixel intensity value of the crack area in the image [31]. But, this pixel intensity is only for the crack depth but not for width and shape of the crack.…”

Section: Introductionmentioning

confidence: 99%

Determination of Geometric Parameters of Cracks in Concrete by Image Processing

Vashpanov

Son

Heo

et al. 2019

Advances in Civil Engineering

View full text Add to dashboard Cite

The 8-bit RGB image of a cracked concrete surface, obtained with a high-resolution camera based on a close-distance photographing and using an optical microscope, is used to estimate the geometrical parameters of the crack. The parameters such as the crack’s width, depth, and morphology can be determined by the pixel intensity distribution of the image. For the estimation, the image is transformed into 16-bit gray scale to enhance the geometrical parameters of the crack and then a mathematical relationship relating the intensity distribution with the depth and width is derived based on the enhanced image. This relationship enables to estimate the width and depth with ±10% and ±15% accuracy, respectively, for the crack samples used for the experiments. It is expected that the accuracy can be further improved if the 8-bit RGB image is synthesized by the images of the cracks obtained with different illumination directions.

show abstract

PMENet: a parallel UNet based on the fusion of multiple attention mechanisms for road crack segmentation

Wang,

Dai,

et al. 2024

SIViP

View full text Add to dashboard Cite

A Study on Crack Depth Measurement in Steel Structures Using Image-Based Intensity Differences

Cited by 5 publications

References 10 publications

Review of Pavement Defect Detection Methods

Review of Pavement Defect Detection Methods

Determination of Geometric Parameters of Cracks in Concrete by Image Processing

PMENet: a parallel UNet based on the fusion of multiple attention mechanisms for road crack segmentation

Contact Info

Product

Resources

About