A Kind of Pavement Crack Detection Method Based on Digital Image Processing

Shao, C.L.; Chen, Yuhang; Xu, Fenghua; Wang, Shenghuai

doi:10.1109/iaeac47372.2019.8997810

Cited by 13 publications

(8 citation statements)

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“…The SPSLM algorithm in the figure is a single-pixel skeleton length measurement algorithm based on the 8-neighborhood model proposed in this paper. Method 1, Method 2, Method 3, and Method 4 are methods for measuring curve lengths used in [ 13 , 14 , 15 ], [ 16 , 17 , 18 , 19 , 20 ], [ 12 ], and [ 21 , 22 ], respectively.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…After obtaining the single-pixel skeleton of the target curve, the method of directly counting the number of pixels as the skeleton length has a large error [ 15 , 16 , 17 ]. To improve the measurement accuracy, this paper proposes a single-pixel skeleton length measurement (SPSLM) algorithm based on the 8-neighborhood model.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…This method is suitable for a rough estimate of extent, but cannot accurately measure fracture length [ 14 ]. Then, after obtaining the single-pixel skeleton of the target curve, the researchers first received the total number of pixels on the structure by counting or integrating, and then multiplied the length of a single pixel to calculate the size of the curve [ 15 , 16 , 17 ]. This method is simple in logic and easy to implement, but oversimplifies the problem, resulting in low calculation accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…This method is simple in logic and easy to implement, but oversimplifies the problem, resulting in low calculation accuracy. To further improve the accuracy, the researchers improved the method which was used in the literature [ 15 , 16 , 17 ] by enhancing the integrand [ 18 ], introducing the idea of displacement [ 19 , 20 ], and classifying the pixels [ 21 , 22 ]. Although the angle of improvement is different, these methods essentially use “

” and “

” to replace the length represented by a pixel and calculate the total distance by accumulating the sizes of all pixels on the skeleton.…”

Section: Introductionmentioning

confidence: 99%

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Threshold Segmentation and Length Measurement Algorithms for Irregular Curves in Complex Backgrounds

Ruan

Deng

et al. 2022

Sensors

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It is an urgent problem to know how to quickly and accurately measure the length of irregular curves in complex background images. To solve the problem, we first proposed a quasi-bimodal threshold segmentation (QBTS) algorithm, which transforms the multimodal histogram into a quasi-bimodal histogram to achieve a faster and more accurate segmentation of the target curve. Then, we proposed a single-pixel skeleton length measurement (SPSLM) algorithm based on the 8-neighborhood model, which used the 8-neighborhood feature to measure the length for the first time, and achieved a more accurate measurement of the curve length. Finally, the two algorithms were tested and analyzed in terms of accuracy and speed on the two original datasets of this paper. The experimental results show that the algorithms proposed in this paper can quickly and accurately segment the target curve from the neon design rendering with complex background interference and measure its length.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Proposed Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

” and “

” to replace the length represented by a pixel and calculate the total distance by accumulating the sizes of all pixels on the skeleton.…”

Section: Introductionmentioning

confidence: 99%

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Threshold Segmentation and Length Measurement Algorithms for Irregular Curves in Complex Backgrounds

Ruan

Deng

et al. 2022

Sensors

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“…It is difficult to make a detailed and unbiased judgment if rapidity is sought, and rapidity is sacrificed if detailed and unbiased judgment is pursued. On the other hand, in recent years, research on the construction of damage inspection systems using image diagnosis, such as morphological image processing (Sun et al, 1 2009; Qi et al, 2 2014; Shao et al, 3 2019), damage assumption by remote sensing (Miura et al, 4 2015; Nazia et al, 5 2017; Xia et al, 6 2019; Bhangale et al, 7 2019; Chen et al, 8 2019), and image classification technology by deep learning (Yeum et al, 9 2016; Nia et al, 10 2017; Gao et al, 11 2018; Guo et al, 12 2019; Okada et al, 13 2019; Ueoka et al, 14 2019; Guo et al, 15 2020) has been conducted. Especially, image diagnosis by deep learning can achieve quick processing without necessarily requiring professional inspectors, and it seems to be possible to diminish the dispersion of results by the experience and skills of the inspectors.…”

Section: Introductionmentioning

confidence: 99%

Study on image diagnosis of timber houses damaged by earthquake using deep learning

Chida

Takahashi

2021

Japan Architectural Review

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Image processing (semantic segmentation and morphological processing) for the imavge diagnosis of timber houses damaged by earthquake was studied and the following three aspects were revealed.1 Comparing the performance of the models trained with real datasets and chromakeyed datasets, the validation correctness of chromakeyed models achieved comparable or better accuracy than real models (up to 1.38 times more accurate in Frequency Weighted Intersection over Union (FWIoU)). Thus, the usefulness of chromakeyed datasets was confirmed for creating image databases for deep learning. 2 In the image diagnosis results, damage could be assessed with the same accuracy as the investigator if the damage rate was calculated to be 60% or more, unless the damage was unrecognizable due to shielding or resolution problems. This implies the feasibility of image diagnosis for quantitative damage assessment of timber houses. 3 Considering the installation of the estimation methodology of the deformation angle into the image diagnosis, the guideline methodology, and previous experimental values were analyzed. If multiple damage degrees were detected, it could be narrowed down to limited estimation results. This suggests the possibility of installation of estimation methodology into the image diagnosis. Keywords damage assessment, deep learning, image diagnosis, image processing, timber houseThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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