Visual detection of screen defects in occlusion and missing scenes
To improve the intelligent level and solve the difficult problem of defect detection in complex scenarios, a visual detection system for screen defects in occluded and missing scenes is constructed based on object detection and matching, as well as image difference technology. The modules for mobile...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Science Press (China Science Publishing & Media Ltd.)
2023-11-01
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| Series: | Shenzhen Daxue xuebao. Ligong ban |
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| Online Access: | https://journal.szu.edu.cn/en/#/digest?ArticleID=2564 |
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| _version_ | 1850038161896374272 |
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| author | YIN Dongfu DU Mingchen HU Tianhao LI Youming ZHANG Xiaohong YU Fei Richard |
| author_facet | YIN Dongfu DU Mingchen HU Tianhao LI Youming ZHANG Xiaohong YU Fei Richard |
| author_sort | YIN Dongfu |
| collection | DOAJ |
| description | To improve the intelligent level and solve the difficult problem of defect detection in complex scenarios, a visual detection system for screen defects in occluded and missing scenes is constructed based on object detection and matching, as well as image difference technology. The modules for mobile phone screen detection, occlusion detection, missing detection, and screen content change detection are established in this system. The YOLOv8n model is used to detect the position of mobile phone screens in images. Multiple cameras and detection boxes are used to filter out obstructed mobile phone screens. Target matching algorithms are used to determine whether there are any missing mobile phone screens, and an improved image difference algorithm is designed to determine whether the displayed content of mobile phone screen has changed. The data is collected at the practical production site, and then annotated, enhanced, and filtered. The target detection model is trained and tested, and the average detection accuracy of mobile screen is 96.8%. The deployment and application of developed model on the production site have been completed. Through corresponding target matching and differential calculation, real-time and accurate detection of mobile phone screen defects under simultaneous processing of multiple videos has been achieved. The application of proposed algorithm can effectively reduce labor costs and improve detection efficiency. The proposed method can be used for electronic screen quality detection in fields such as mobile phones, computers and televisions. |
| format | Article |
| id | doaj-art-c262f2bdbf124d2280c65d0016e08511 |
| institution | DOAJ |
| issn | 1000-2618 |
| language | English |
| publishDate | 2023-11-01 |
| publisher | Science Press (China Science Publishing & Media Ltd.) |
| record_format | Article |
| series | Shenzhen Daxue xuebao. Ligong ban |
| spelling | doaj-art-c262f2bdbf124d2280c65d0016e085112025-08-20T02:56:39ZengScience Press (China Science Publishing & Media Ltd.)Shenzhen Daxue xuebao. Ligong ban1000-26182023-11-0140663163910.3724/SP.J.1249.2023.066311000-2618（2023）06-0631-09Visual detection of screen defects in occlusion and missing scenesYIN DongfuDU MingchenHU TianhaoLI YoumingZHANG XiaohongYU Fei RichardTo improve the intelligent level and solve the difficult problem of defect detection in complex scenarios, a visual detection system for screen defects in occluded and missing scenes is constructed based on object detection and matching, as well as image difference technology. The modules for mobile phone screen detection, occlusion detection, missing detection, and screen content change detection are established in this system. The YOLOv8n model is used to detect the position of mobile phone screens in images. Multiple cameras and detection boxes are used to filter out obstructed mobile phone screens. Target matching algorithms are used to determine whether there are any missing mobile phone screens, and an improved image difference algorithm is designed to determine whether the displayed content of mobile phone screen has changed. The data is collected at the practical production site, and then annotated, enhanced, and filtered. The target detection model is trained and tested, and the average detection accuracy of mobile screen is 96.8%. The deployment and application of developed model on the production site have been completed. Through corresponding target matching and differential calculation, real-time and accurate detection of mobile phone screen defects under simultaneous processing of multiple videos has been achieved. The application of proposed algorithm can effectively reduce labor costs and improve detection efficiency. The proposed method can be used for electronic screen quality detection in fields such as mobile phones, computers and televisions.https://journal.szu.edu.cn/en/#/digest?ArticleID=2564artificial intelligenceimage processingtarget detectiondefect detectiontarget matchingimage differentiation |
| spellingShingle | YIN Dongfu DU Mingchen HU Tianhao LI Youming ZHANG Xiaohong YU Fei Richard Visual detection of screen defects in occlusion and missing scenes Shenzhen Daxue xuebao. Ligong ban artificial intelligence image processing target detection defect detection target matching image differentiation |
| title | Visual detection of screen defects in occlusion and missing scenes |
| title_full | Visual detection of screen defects in occlusion and missing scenes |
| title_fullStr | Visual detection of screen defects in occlusion and missing scenes |
| title_full_unstemmed | Visual detection of screen defects in occlusion and missing scenes |
| title_short | Visual detection of screen defects in occlusion and missing scenes |
| title_sort | visual detection of screen defects in occlusion and missing scenes |
| topic | artificial intelligence image processing target detection defect detection target matching image differentiation |
| url | https://journal.szu.edu.cn/en/#/digest?ArticleID=2564 |
| work_keys_str_mv | AT yindongfu visualdetectionofscreendefectsinocclusionandmissingscenes AT dumingchen visualdetectionofscreendefectsinocclusionandmissingscenes AT hutianhao visualdetectionofscreendefectsinocclusionandmissingscenes AT liyouming visualdetectionofscreendefectsinocclusionandmissingscenes AT zhangxiaohong visualdetectionofscreendefectsinocclusionandmissingscenes AT yufeirichard visualdetectionofscreendefectsinocclusionandmissingscenes |