The improved C3STR structure.
<div><p>Aiming at the problems of low accuracy and large computation in the task of PCB defect detection. This paper proposes a lightweight PCB defect detection algorithm based on YOLO. To address the problem of large numbers of parameters and calculations, GhostNet are used in Backbone...
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| مؤلفون آخرون: | , , , , |
| منشور في: |
2025
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| _version_ | 1852021197173161984 |
|---|---|
| author | Shiwei Yu (6060308) |
| author2 | Feng Pan (257056) Xiaoqiang Zhang (2413909) Linhua Zhou (6430619) Liang Zhang (41392) Jikui Wang (4519225) |
| author2_role | author author author author author |
| author_facet | Shiwei Yu (6060308) Feng Pan (257056) Xiaoqiang Zhang (2413909) Linhua Zhou (6430619) Liang Zhang (41392) Jikui Wang (4519225) |
| author_role | author |
| dc.creator.none.fl_str_mv | Shiwei Yu (6060308) Feng Pan (257056) Xiaoqiang Zhang (2413909) Linhua Zhou (6430619) Liang Zhang (41392) Jikui Wang (4519225) |
| dc.date.none.fl_str_mv | 2025-04-18T17:25:48Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0320344.g005 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/The_improved_C3STR_structure_/28825334 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified xlink "> aiming simple defect types pcb defect detection lightweight detection algorithm depthwise separable convolution redundant parameters within model &# 8217 yolo </ p panet network structure ordinary convolution model lightweight scale features results indicated parameter count paper proposes original model large numbers large computation defective images confusion caused cluttered background c3str module c3 module better suited arithmetic platforms |
| dc.title.none.fl_str_mv | The improved C3STR structure. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <div><p>Aiming at the problems of low accuracy and large computation in the task of PCB defect detection. This paper proposes a lightweight PCB defect detection algorithm based on YOLO. To address the problem of large numbers of parameters and calculations, GhostNet are used in Backbone to keep the model lightweight. Second, the ordinary convolution of the neck network is improved by depthwise separable convolution, resulting in a reduction of redundant parameters within the neck network. Afterwards, the Swin-Transformer is integrated with the C3 module in the Neck to build the C3STR module, which aims to address the issue of cluttered background in defective images and the confusion caused by simple defect types. Finally, the PANet network structure is replaced with the bidirectional feature pyramid network (BIFPN) structure to enhance the fusion of multi-scale features in the network. The results indicated that when comparing our model with the original model, there was a 47.2% reduction in the model’s parameter count, a 48.5% reduction in GFLOPs, a 42.4% reduction in Weight, a 2.0% reduction in FPS, and a 2.4% rise in mAP. The model is better suited for use on low-arithmetic platforms as a result.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_bd0bbcd2bc9900b035dd39a08c6ab759 |
| identifier_str_mv | 10.1371/journal.pone.0320344.g005 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/28825334 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | The improved C3STR structure.Shiwei Yu (6060308)Feng Pan (257056)Xiaoqiang Zhang (2413909)Linhua Zhou (6430619)Liang Zhang (41392)Jikui Wang (4519225)Space ScienceEnvironmental Sciences not elsewhere classifiedBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedxlink "> aimingsimple defect typespcb defect detectionlightweight detection algorithmdepthwise separable convolutionredundant parameters withinmodel &# 8217yolo </ ppanet network structureordinary convolutionmodel lightweightscale featuresresults indicatedparameter countpaper proposesoriginal modellarge numberslarge computationdefective imagesconfusion causedcluttered backgroundc3str modulec3 modulebetter suitedarithmetic platforms<div><p>Aiming at the problems of low accuracy and large computation in the task of PCB defect detection. This paper proposes a lightweight PCB defect detection algorithm based on YOLO. To address the problem of large numbers of parameters and calculations, GhostNet are used in Backbone to keep the model lightweight. Second, the ordinary convolution of the neck network is improved by depthwise separable convolution, resulting in a reduction of redundant parameters within the neck network. Afterwards, the Swin-Transformer is integrated with the C3 module in the Neck to build the C3STR module, which aims to address the issue of cluttered background in defective images and the confusion caused by simple defect types. Finally, the PANet network structure is replaced with the bidirectional feature pyramid network (BIFPN) structure to enhance the fusion of multi-scale features in the network. The results indicated that when comparing our model with the original model, there was a 47.2% reduction in the model’s parameter count, a 48.5% reduction in GFLOPs, a 42.4% reduction in Weight, a 2.0% reduction in FPS, and a 2.4% rise in mAP. The model is better suited for use on low-arithmetic platforms as a result.</p></div>2025-04-18T17:25:48ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0320344.g005https://figshare.com/articles/figure/The_improved_C3STR_structure_/28825334CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/288253342025-04-18T17:25:48Z |
| spellingShingle | The improved C3STR structure. Shiwei Yu (6060308) Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified xlink "> aiming simple defect types pcb defect detection lightweight detection algorithm depthwise separable convolution redundant parameters within model &# 8217 yolo </ p panet network structure ordinary convolution model lightweight scale features results indicated parameter count paper proposes original model large numbers large computation defective images confusion caused cluttered background c3str module c3 module better suited arithmetic platforms |
| status_str | publishedVersion |
| title | The improved C3STR structure. |
| title_full | The improved C3STR structure. |
| title_fullStr | The improved C3STR structure. |
| title_full_unstemmed | The improved C3STR structure. |
| title_short | The improved C3STR structure. |
| title_sort | The improved C3STR structure. |
| topic | Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified xlink "> aiming simple defect types pcb defect detection lightweight detection algorithm depthwise separable convolution redundant parameters within model &# 8217 yolo </ p panet network structure ordinary convolution model lightweight scale features results indicated parameter count paper proposes original model large numbers large computation defective images confusion caused cluttered background c3str module c3 module better suited arithmetic platforms |