Evaluation and Comparison of Thresholding Segmentation Techniques

Dong, Yubing; Li, Ming Jing; Cheng, Guang Liang

doi:10.4028/www.scientific.net/amm.519-520.687

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2021

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“…e effects of detection methods are mainly focused on the sampledata cluster formation protocol to tackle the leaderless cluster formation problems [1]. ey are mainly divided into traditional image processing methods (including edge detection [2], threshold segmentation [3], and region segmentation [4]) and machine learning methods (including deep learning [5] and clustering segmentation [6]).…”

Section: Introductionmentioning

confidence: 99%

Semantic Recognition and Location of Cracks by Fusing Cracks Segmentation and Deep Learning

Chen

et al. 2021

Complexity

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For a long time, cracks can appear on the surface of concrete, resulting in a number of safety problems. Traditional manual detection methods not only cost money and time but also cannot guarantee high accuracy. Therefore, a recognition method based on the combination of convolutional neural network and cluster segmentation is proposed. The proposed method realizes the accurate identification of concrete surface crack image under complex background and improves the efficiency of concrete surface crack identification. The research results show that the proposed method not only classifies crack and noncrack efficiently but also identifies cracks in complex backgrounds. The proposed method has high accuracy in crack recognition, which is at least 97.3% and even up to 98.6%.

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Section: Introductionmentioning

confidence: 99%

Semantic Recognition and Location of Cracks by Fusing Cracks Segmentation and Deep Learning

Chen

et al. 2021

Complexity

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Evaluation and Comparison of Thresholding Segmentation Techniques

Cited by 1 publication

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Semantic Recognition and Location of Cracks by Fusing Cracks Segmentation and Deep Learning

Semantic Recognition and Location of Cracks by Fusing Cracks Segmentation and Deep Learning

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