Overall structure of the model.
<div><p>Drum roller surface defect detection is of great research significance for control production quality. Aiming at solving the problems that the traditional light source visual imaging system, which does not clearly reflect defect features, the defect detection efficiency is low, a...
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2025
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| _version_ | 1852022999256924160 |
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| author | Guofeng Qin (11025170) |
| author2 | Qinkai Zou (20678485) Mengyan Li (1858759) Yi Deng (421971) Peiwen Mi (20678488) Yongjian Zhu (6562817) Hao Liu (12832) |
| author2_role | author author author author author author |
| author_facet | Guofeng Qin (11025170) Qinkai Zou (20678485) Mengyan Li (1858759) Yi Deng (421971) Peiwen Mi (20678488) Yongjian Zhu (6562817) Hao Liu (12832) |
| author_role | author |
| dc.creator.none.fl_str_mv | Guofeng Qin (11025170) Qinkai Zou (20678485) Mengyan Li (1858759) Yi Deng (421971) Peiwen Mi (20678488) Yongjian Zhu (6562817) Hao Liu (12832) |
| dc.date.none.fl_str_mv | 2025-02-05T18:32:13Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0316569.g007 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Overall_structure_of_the_model_/28353919 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Science Policy Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified model &# 8217 great research significance feature extraction ability experimental results show broader dataset diversity traditional light source achieve efficient detection using enhanced yolov8n defect detection efficiency main average accuracy industrial drum rollers improved yolov8n algorithm control production quality surface defect detection system light source production quality drum rollers structured light defect characteristics detection time detection accuracy rolling surface algorithm designed quality samples drum roller standard convolution replace ciou network pays loss function image acquisition fusion network certain improvement better ensure backbone network 2 %, |
| dc.title.none.fl_str_mv | Overall structure of the model. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <div><p>Drum roller surface defect detection is of great research significance for control production quality. Aiming at solving the problems that the traditional light source visual imaging system, which does not clearly reflect defect features, the defect detection efficiency is low, and the accuracy is not enough, this paper designs an image acquisition system based on line fringe structured light and proposes an improved deep learning network model based on YOLOv8n to achieve efficient detection of defects on the rolling surface of a drum roller. In the aspect of image acquisition, this paper selected the line fringe structured light as the system light source, which made up for the problem that the traditional light source does not reflect the defect characteristics. In terms of algorithms, firstly, using deformable convolution instead of standard convolution to enhance the feature extraction ability of the backbone network. Then, a new feature fusion module was proposed to enable the fusion network to learn additional original information. Finally, Wise-IoU was applied to replace CIoU in the loss function, so that the network pays more attention to the high-quality samples. The experimental results show that the improved YOLOv8n algorithm has a certain improvement in detection accuracy. The main average accuracy (mAP) is 97.2%, and the detection time is 4.3ms. The system and algorithm designed in this paper can better ensure the production quality of drum rollers. While effective, the model’s standard rectangular bounding boxes may limit precision for elongated defects. Future work could explore rotated bounding boxes and broader dataset diversity to enhance generalization in real-world applications.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_4c4161f65fca595bd7219c9d1f7ff080 |
| identifier_str_mv | 10.1371/journal.pone.0316569.g007 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/28353919 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Overall structure of the model.Guofeng Qin (11025170)Qinkai Zou (20678485)Mengyan Li (1858759)Yi Deng (421971)Peiwen Mi (20678488)Yongjian Zhu (6562817)Hao Liu (12832)Science PolicySpace ScienceBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedmodel &# 8217great research significancefeature extraction abilityexperimental results showbroader dataset diversitytraditional light sourceachieve efficient detectionusing enhanced yolov8ndefect detection efficiencymain average accuracyindustrial drum rollersimproved yolov8n algorithmcontrol production qualitysurface defect detectionsystem light sourceproduction qualitydrum rollersstructured lightdefect characteristicsdetection timedetection accuracyrolling surfacealgorithm designedquality samplesdrum rollerstandard convolutionreplace ciounetwork paysloss functionimage acquisitionfusion networkcertain improvementbetter ensurebackbone network2 %,<div><p>Drum roller surface defect detection is of great research significance for control production quality. Aiming at solving the problems that the traditional light source visual imaging system, which does not clearly reflect defect features, the defect detection efficiency is low, and the accuracy is not enough, this paper designs an image acquisition system based on line fringe structured light and proposes an improved deep learning network model based on YOLOv8n to achieve efficient detection of defects on the rolling surface of a drum roller. In the aspect of image acquisition, this paper selected the line fringe structured light as the system light source, which made up for the problem that the traditional light source does not reflect the defect characteristics. In terms of algorithms, firstly, using deformable convolution instead of standard convolution to enhance the feature extraction ability of the backbone network. Then, a new feature fusion module was proposed to enable the fusion network to learn additional original information. Finally, Wise-IoU was applied to replace CIoU in the loss function, so that the network pays more attention to the high-quality samples. The experimental results show that the improved YOLOv8n algorithm has a certain improvement in detection accuracy. The main average accuracy (mAP) is 97.2%, and the detection time is 4.3ms. The system and algorithm designed in this paper can better ensure the production quality of drum rollers. While effective, the model’s standard rectangular bounding boxes may limit precision for elongated defects. Future work could explore rotated bounding boxes and broader dataset diversity to enhance generalization in real-world applications.</p></div>2025-02-05T18:32:13ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0316569.g007https://figshare.com/articles/figure/Overall_structure_of_the_model_/28353919CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/283539192025-02-05T18:32:13Z |
| spellingShingle | Overall structure of the model. Guofeng Qin (11025170) Science Policy Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified model &# 8217 great research significance feature extraction ability experimental results show broader dataset diversity traditional light source achieve efficient detection using enhanced yolov8n defect detection efficiency main average accuracy industrial drum rollers improved yolov8n algorithm control production quality surface defect detection system light source production quality drum rollers structured light defect characteristics detection time detection accuracy rolling surface algorithm designed quality samples drum roller standard convolution replace ciou network pays loss function image acquisition fusion network certain improvement better ensure backbone network 2 %, |
| status_str | publishedVersion |
| title | Overall structure of the model. |
| title_full | Overall structure of the model. |
| title_fullStr | Overall structure of the model. |
| title_full_unstemmed | Overall structure of the model. |
| title_short | Overall structure of the model. |
| title_sort | Overall structure of the model. |
| topic | Science Policy Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified model &# 8217 great research significance feature extraction ability experimental results show broader dataset diversity traditional light source achieve efficient detection using enhanced yolov8n defect detection efficiency main average accuracy industrial drum rollers improved yolov8n algorithm control production quality surface defect detection system light source production quality drum rollers structured light defect characteristics detection time detection accuracy rolling surface algorithm designed quality samples drum roller standard convolution replace ciou network pays loss function image acquisition fusion network certain improvement better ensure backbone network 2 %, |