Pavement damage identification and evaluation in UAV-captured images using gray level co-occurrence matrix and cloud model

He, Jiawei; Shao, Lei; Li, Yufang; Wang, Kai; Liu, Weihua

doi:10.1016/j.jksuci.2023.101762

Cited by 2 publications

(2 citation statements)

References 26 publications

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“…Gabor filters, for instance, are adept at capturing both fine and coarse textures by analyzing local spatial frequency contents. The Gray Level Co-occurrence Matrix (GLCM), on the other hand, offers insights into the statistical relationships among pixel values, providing a measure of homogeneity, contrast, and entropy [117,118]. Wavelet transforms enable a multi-scale analysis, unraveling textures at different frequency bands.…”

Section: Texture Analysismentioning

confidence: 99%

Review of Image-Processing-Based Technology for Structural Health Monitoring of Civil Infrastructures

Kim,

Choi,

Kim

et al. 2024

J. Imaging

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The continuous monitoring of civil infrastructures is crucial for ensuring public safety and extending the lifespan of structures. In recent years, image-processing-based technologies have emerged as powerful tools for the structural health monitoring (SHM) of civil infrastructures. This review provides a comprehensive overview of the advancements, applications, and challenges associated with image processing in the field of SHM. The discussion encompasses various imaging techniques such as satellite imagery, Light Detection and Ranging (LiDAR), optical cameras, and other non-destructive testing methods. Key topics include the use of image processing for damage detection, crack identification, deformation monitoring, and overall structural assessment. This review explores the integration of artificial intelligence and machine learning techniques with image processing for enhanced automation and accuracy in SHM. By consolidating the current state of image-processing-based technology for SHM, this review aims to show the full potential of image-based approaches for researchers, engineers, and professionals involved in civil engineering, SHM, image processing, and related fields.

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Section: Texture Analysismentioning

confidence: 99%

Review of Image-Processing-Based Technology for Structural Health Monitoring of Civil Infrastructures

Kim,

Choi,

Kim

et al. 2024

J. Imaging

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“…Furthermore, in the evaluation of the skidding resistance of asphalt pavements based on texture features, Lu et al [13] discussed evaluation indicators by executing generative adversarial networks (GANs) to quantitatively estimate the diversity of the reconstructed textures. Hu et al [14] evaluated the influence of macrotextural features on the antiskidding performance of asphalt mixtures with different gradations based on the Bayesian light GBM model.…”

Section: Introductionmentioning

confidence: 99%

Research on the Skidding Resistance and Attenuation Characteristics of Asphalt Pavement Based on Image Recognition-Analysis Strategy

Zhang,

Xi,

Zhao

et al. 2024

Coatings

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To accurately evaluate the skidding resistance of asphalt pavements, a texture imaging device was developed to realize the standardized acquisition of pavement images. Based on the gray-level co-occurrence matrix and multifractal theory of texture structure, the influence of segregation degree and gradation type on the texture properties of asphalt pavement was studied. Meanwhile, a comprehensive evaluation index of skidding resistance was proposed for asphalt pavement. Furthermore, the attenuation characteristics of the anti-skidding performance for asphalt mixture were explored, and the corresponding attenuation model of asphalt pavement was established. The results show that the segregation degree and gradation type significantly affected the texture parameters and anti-skidding performance of asphalt mixture. Specially, with an increase in the segregation degree of coarse aggregate, the parameters of energy, entropy, and multifractal spectrum width gradually increased, whereas the inertial moment gradually decreased. The variation range of the multifractal spectrum difference initially increased and subsequently decreased. For the texture parameters such as energy, entropy, inertial moment, and multifractal spectrum width Δα, the values of the asphalt mixture with larger nominal maximum particle were higher than those of the mixture with smaller nominal maximum particle, whereas the multifractal spectrum difference value showed the opposite law. In addition, the texture parameters of energy, entropy, and multifractal spectrum width exhibited good linear correlation with the texture depth (TD) of asphalt mixtures with various segregation levels and gradation types. The index based on the texture parameters of energy, entropy, and multifractal spectrum width effectively evaluated the skidding resistance of asphalt pavements, which showed the same trend as the TD with the increase of the abrasion number. The achievement provides an effective solution for the evaluation of skidding resistance and attenuation characteristics of asphalt mixtures.

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Pavement damage identification and evaluation in UAV-captured images using gray level co-occurrence matrix and cloud model

Cited by 2 publications

References 26 publications

Review of Image-Processing-Based Technology for Structural Health Monitoring of Civil Infrastructures

Review of Image-Processing-Based Technology for Structural Health Monitoring of Civil Infrastructures

Research on the Skidding Resistance and Attenuation Characteristics of Asphalt Pavement Based on Image Recognition-Analysis Strategy

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