Parameter Adaptive LCD Screen Defect Detection Framework
It is necessary to detect defects in the production process of LCD screens for quality improvements Manual detection brings a heavy workload and low accuracy Therefore, an efficient and accurate automatic detection method is urgently needed To this end, this paper proposes a new defect detection fra...
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| Format: | Article |
| Language: | zho |
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Harbin University of Science and Technology Publications
2020-10-01
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| Series: | Journal of Harbin University of Science and Technology |
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| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=1872 |
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| _version_ | 1849398121163915264 |
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| author | LIU Wang SHAO Huili HE Yongjun XIE Yining CHEN Deyun |
| author_facet | LIU Wang SHAO Huili HE Yongjun XIE Yining CHEN Deyun |
| author_sort | LIU Wang |
| collection | DOAJ |
| description | It is necessary to detect defects in the production process of LCD screens for quality improvements Manual detection brings a heavy workload and low accuracy Therefore, an efficient and accurate automatic detection method is urgently needed To this end, this paper proposes a new defect detection framework, which mainly includes screen area extraction, preprocessing, threshold segmentation and defect selection By adaptive adjustment of parameters, the detection method can adapt to various complex situations In order to eliminate the influence of illumination changes, the defect region is segmented by automatic parameter adjustment in the threshold segmentation First, the maximum grayscale value of the image is calculated, and then the fixed parameters and the coefficient of the defect image are determined according to the nodefect image, and finally the maximum value which was selected as the minimum threshold of the threshold segmentation from the fixed parameters and the product of the maximum grayscale value and the coefficient In addition, in order to solve the problem that the brightness difference of the images captured by lowresolution cameras is too small to detect defects in the saturation condition, selfadaptive adjustment of exposure parameters was used to collect images to process different parts of images with large difference in light and shade Experiments show that the method can achieve high performance and efficiency in detecting defects such as points, lines, Mura, saturation |
| format | Article |
| id | doaj-art-a9c645f2cc8042c48fafb59c3c7b9588 |
| institution | Kabale University |
| issn | 1007-2683 |
| language | zho |
| publishDate | 2020-10-01 |
| publisher | Harbin University of Science and Technology Publications |
| record_format | Article |
| series | Journal of Harbin University of Science and Technology |
| spelling | doaj-art-a9c645f2cc8042c48fafb59c3c7b95882025-08-20T03:38:43ZzhoHarbin University of Science and Technology PublicationsJournal of Harbin University of Science and Technology1007-26832020-10-012505758210.15938/j.jhust.2020.05.011Parameter Adaptive LCD Screen Defect Detection FrameworkLIU Wang0SHAO Huili1HE Yongjun2XIE Yining3CHEN Deyun4School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, ChinaSchool of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, ChinaSchool of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, ChinaSchool of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, ChinaSchool of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, ChinaIt is necessary to detect defects in the production process of LCD screens for quality improvements Manual detection brings a heavy workload and low accuracy Therefore, an efficient and accurate automatic detection method is urgently needed To this end, this paper proposes a new defect detection framework, which mainly includes screen area extraction, preprocessing, threshold segmentation and defect selection By adaptive adjustment of parameters, the detection method can adapt to various complex situations In order to eliminate the influence of illumination changes, the defect region is segmented by automatic parameter adjustment in the threshold segmentation First, the maximum grayscale value of the image is calculated, and then the fixed parameters and the coefficient of the defect image are determined according to the nodefect image, and finally the maximum value which was selected as the minimum threshold of the threshold segmentation from the fixed parameters and the product of the maximum grayscale value and the coefficient In addition, in order to solve the problem that the brightness difference of the images captured by lowresolution cameras is too small to detect defects in the saturation condition, selfadaptive adjustment of exposure parameters was used to collect images to process different parts of images with large difference in light and shade Experiments show that the method can achieve high performance and efficiency in detecting defects such as points, lines, Mura, saturationhttps://hlgxb.hrbust.edu.cn/#/digest?ArticleID=1872defect detectiondifference of gaussiansaturationparameter adaptive |
| spellingShingle | LIU Wang SHAO Huili HE Yongjun XIE Yining CHEN Deyun Parameter Adaptive LCD Screen Defect Detection Framework Journal of Harbin University of Science and Technology defect detection difference of gaussian saturation parameter adaptive |
| title | Parameter Adaptive LCD Screen Defect Detection Framework |
| title_full | Parameter Adaptive LCD Screen Defect Detection Framework |
| title_fullStr | Parameter Adaptive LCD Screen Defect Detection Framework |
| title_full_unstemmed | Parameter Adaptive LCD Screen Defect Detection Framework |
| title_short | Parameter Adaptive LCD Screen Defect Detection Framework |
| title_sort | parameter adaptive lcd screen defect detection framework |
| topic | defect detection difference of gaussian saturation parameter adaptive |
| url | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=1872 |
| work_keys_str_mv | AT liuwang parameteradaptivelcdscreendefectdetectionframework AT shaohuili parameteradaptivelcdscreendefectdetectionframework AT heyongjun parameteradaptivelcdscreendefectdetectionframework AT xieyining parameteradaptivelcdscreendefectdetectionframework AT chendeyun parameteradaptivelcdscreendefectdetectionframework |