Automated crack segmentation via saturation channel thresholding, area classification and fusion of modified level set segmentation with Canny edge detection

Nnolim, Uche A.

doi:10.1016/j.heliyon.2020.e05748

Cited by 32 publications

(20 citation statements)

References 53 publications

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“…It is an image processing technique to detect brightness discontinuities, extracting large gradient brightness magnitudes [26]. This method yields the best result than other edge detection methods because it can effectively suppress the noise and detect it well [27].…”

Section: )mentioning

confidence: 99%

Implementation of Gabor Wavelet and Support Vector Machine for Braille Recognition

Perwira¹,

Florestiyanto²,

Nurjanah³

et al. 2022

International Journal on Advanced Science, Engineering and Information Technology

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Visually impaired people use braille letters to write and read, which not all people with normal vision can read. It causes parents of children with impaired vision to have difficulties in assisting them in learning at home. At the same time, the involvement of parents in children's learning assistance is needed to monitor their learning progress. In this research, braille letters are identified through images taken using a scanner as a tool to input the images. Then, the Canny edge detection method is used obtain all the edges of each braille dot. Feature extraction is applied to obtain all characteristics of each letter, and the method used is the Gabor Wavelet. The features which are utilized include standard deviation, mean, variance, and median with a theta angle of 0 0 , 30 0 , 45 0 , 60 0 , 120 0 , 135 0 , 180 0 and wavelengths of 3, 6, 13, 28, and 58. These features are combined and used as test data and training data for the Support Vector Machine (SVM) classification stage and produce letters and words in alphabetic letter forms. Braille letters that can be identified in this research are small letters, capital letters, punctuation marks, and numbers. Tests are carried out using a multi-class confusion matrix scenario to determine the level of accuracy, precision, and recall. Based on the results of the tests conducted using 758 braille letters, the accuracy value is 98.15%; the precision value is 97.66%; and the recall value is 98.28%.

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Section: )mentioning

confidence: 99%

Implementation of Gabor Wavelet and Support Vector Machine for Braille Recognition

Perwira¹,

Florestiyanto²,

Nurjanah³

et al. 2022

International Journal on Advanced Science, Engineering and Information Technology

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“…Finally, the boundary of the wood in the image was found and the panel image was cut out. Canny edge detection [28] technology is applied in image processing [29] and has been widely used in traffic accident detection [30], crack automatic segmentation [31], intelligent traffic management [32], and other fields. The solid wood panel image was extracted from the black background, and the influence of the background was removed.…”

Section: Preprocessing Imagesmentioning

confidence: 99%

Color Classification and Texture Recognition System of Solid Wood Panels

Wang

Zhuang

Liu

et al. 2021

Forests

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Solid wood panels are widely used in the wood flooring and furniture industries, and paneling is an excellent material for indoor decoration. The classification of colors helps to improve the appearance of wood products assembled from multiple panels due to the differences in surface colors of solid wood panels. Traditional wood surface color classification mainly depends on workers’ visual observations, and manual color classification is prone to visual fatigue and quality instability. In order to reduce labor costs of sorting and to improve production efficiency, in this study, we introduced machine vision technology and an unsupervised learning technique. First-order color moments, second-order color moments, and color histogram peaks were selected to extract feature vectors and to realize data dimension reduction. The feature vector set was divided into different clusters by the K-means algorithm to achieve color classification and, thus, the solid wood panels with similar surface color were classified into one category. Furthermore, during twice clustering based on second-order color moment, texture recognition was realized on the basis of color classification. A sample of beech wood was selected as the research object, not only was color classification completed, but texture recognition was also realized. The experimental results verified the effectiveness of the technical proposal.

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“…However, this problem is solved with the improved CIEAD-2 technique. Yet another study [7] proposes automated crack segmentation via saturation channel thresholding, area classification, and a fusion of a modified-level set segmentation with Canny edge detection. It is demonstrated that this approach is able to identify cracks with nonuniform lightning (Figure 16, 3rd row, [7]).…”

Section: Introductionmentioning

confidence: 99%

“…Yet another study [7] proposes automated crack segmentation via saturation channel thresholding, area classification, and a fusion of a modified-level set segmentation with Canny edge detection. It is demonstrated that this approach is able to identify cracks with nonuniform lightning (Figure 16, 3rd row, [7]). However, its applicability for images contaminated by high intensity optical shadows is not investigated.…”

Section: Introductionmentioning

confidence: 99%

“…However, its applicability for images contaminated by high intensity optical shadows is not investigated. Moreover, the segmentation of images with rough surfaces and uniform illumination might not provide robust results in some cases-rough regions in the background sometimes can be identified as micro-cracks (Figure 18, part g, 2nd row, [7]). However, this problem might be solved by fine tuning parameters of the proposed concrete crack processing algorithm.…”

Section: Introductionmentioning

confidence: 99%

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An Overview of Challenges Associated with Automatic Detection of Concrete Cracks in the Presence of Shadows

et al. 2021

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Detection and assessment of cracks in civil engineering structures such as roads, bridges, dams and pipelines are crucial tasks for maintaining the safety and cost-effectiveness of those concrete structures. With the recent advances in machine learning, the development of ANN- and CNN-based algorithms has become a popular approach for the automated detection and identification of concrete cracks. However, most of the proposed models are trained on images taken in ideal conditions and are only capable of achieving high accuracy when applied to the concrete images devoid of irregular illumination conditions, shadows, shading, blemishes, etc. An overview of challenges related to the automatic detection of concrete cracks in the presence of shadows is presented in this paper. In particular, difficulties associated with the application of deep learning-based methods for the classification of concrete images with shadows are demonstrated. Moreover, the limitations of the shadow removal techniques for the improvement of the crack detection accuracy are discussed.

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Automated crack segmentation via saturation channel thresholding, area classification and fusion of modified level set segmentation with Canny edge detection

Cited by 32 publications

References 53 publications

Implementation of Gabor Wavelet and Support Vector Machine for Braille Recognition

Implementation of Gabor Wavelet and Support Vector Machine for Braille Recognition

Color Classification and Texture Recognition System of Solid Wood Panels

An Overview of Challenges Associated with Automatic Detection of Concrete Cracks in the Presence of Shadows

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