Surface Defect Detection of Seals Based on K-Means Clustering Algorithm and Particle Swarm Optimization

As an important part of automobile, the quality and safety of automobile engine high-pressure oil circuit seal parts are an important indicator of the manufacturer’s production process. In order to improve the detection accuracy and efficiency of seal parts in the traditional production process, the...

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Autores principales:	Xiaoguang Li, Juan Zhu, Haoran Shi, Zijian Cong
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/cf53303132214783aa9ede1297ac2d06
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spelling	oai:doaj.org-article:cf53303132214783aa9ede1297ac2d062021-11-08T02:36:57ZSurface Defect Detection of Seals Based on K-Means Clustering Algorithm and Particle Swarm Optimization1875-919X10.1155/2021/3965247https://doaj.org/article/cf53303132214783aa9ede1297ac2d062021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/3965247https://doaj.org/toc/1875-919XAs an important part of automobile, the quality and safety of automobile engine high-pressure oil circuit seal parts are an important indicator of the manufacturer’s production process. In order to improve the detection accuracy and efficiency of seal parts in the traditional production process, the defect detection method on the surface of the seal was studied. A K-Means clustering image segmentation algorithm based on particle swarm optimization was proposed. To detect the surface defects of seals, first, preprocess the seal image. Then, use the SURF algorithm to extract the feature points of the seal image. Finally, according to the particle swarm fitness variance function, select the insertion point calculated by combining particle swarm optimization and K-Means algorithm. Through iteration, optimize the initial clustering center of K-Means algorithm. The efficiency of K-Means algorithm clustering iteration is improved. The test verifies the applicability of the algorithm in the actual process, and it can be used to accurately detect seals. Experimental results show that the detection accuracy rate reaches 98%, which is highly applicable to the actual production.Xiaoguang LiJuan ZhuHaoran ShiZijian CongHindawi LimitedarticleComputer softwareQA76.75-76.765ENScientific Programming, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer software QA76.75-76.765
spellingShingle	Computer software QA76.75-76.765 Xiaoguang Li Juan Zhu Haoran Shi Zijian Cong Surface Defect Detection of Seals Based on K-Means Clustering Algorithm and Particle Swarm Optimization
description	As an important part of automobile, the quality and safety of automobile engine high-pressure oil circuit seal parts are an important indicator of the manufacturer’s production process. In order to improve the detection accuracy and efficiency of seal parts in the traditional production process, the defect detection method on the surface of the seal was studied. A K-Means clustering image segmentation algorithm based on particle swarm optimization was proposed. To detect the surface defects of seals, first, preprocess the seal image. Then, use the SURF algorithm to extract the feature points of the seal image. Finally, according to the particle swarm fitness variance function, select the insertion point calculated by combining particle swarm optimization and K-Means algorithm. Through iteration, optimize the initial clustering center of K-Means algorithm. The efficiency of K-Means algorithm clustering iteration is improved. The test verifies the applicability of the algorithm in the actual process, and it can be used to accurately detect seals. Experimental results show that the detection accuracy rate reaches 98%, which is highly applicable to the actual production.
format	article
author	Xiaoguang Li Juan Zhu Haoran Shi Zijian Cong
author_facet	Xiaoguang Li Juan Zhu Haoran Shi Zijian Cong
author_sort	Xiaoguang Li
title	Surface Defect Detection of Seals Based on K-Means Clustering Algorithm and Particle Swarm Optimization
title_short	Surface Defect Detection of Seals Based on K-Means Clustering Algorithm and Particle Swarm Optimization
title_full	Surface Defect Detection of Seals Based on K-Means Clustering Algorithm and Particle Swarm Optimization
title_fullStr	Surface Defect Detection of Seals Based on K-Means Clustering Algorithm and Particle Swarm Optimization
title_full_unstemmed	Surface Defect Detection of Seals Based on K-Means Clustering Algorithm and Particle Swarm Optimization
title_sort	surface defect detection of seals based on k-means clustering algorithm and particle swarm optimization
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/cf53303132214783aa9ede1297ac2d06
work_keys_str_mv	AT xiaoguangli surfacedefectdetectionofsealsbasedonkmeansclusteringalgorithmandparticleswarmoptimization AT juanzhu surfacedefectdetectionofsealsbasedonkmeansclusteringalgorithmandparticleswarmoptimization AT haoranshi surfacedefectdetectionofsealsbasedonkmeansclusteringalgorithmandparticleswarmoptimization AT zijiancong surfacedefectdetectionofsealsbasedonkmeansclusteringalgorithmandparticleswarmoptimization
_version_	1718443139548577792

Surface Defect Detection of Seals Based on K-Means Clustering Algorithm and Particle Swarm Optimization

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