A comparison chart of the detection effect.
<div><p>The improved YOLOv8n algorithm is proposed for the existing target detection algorithms to solve the issues of insufficient detection accuracy and leakage due to the target scale variability and complex background interference during road surface crack detection. This algorithm i...
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| منشور في: |
2025
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| _version_ | 1852016965381521408 |
|---|---|
| author | Wenyuan Xu (2553028) |
| author2 | Jianbo Xu (1824238) Yongcheng Ji (5628128) Guodong Li (1525873) Hao Li (31608) Zhen Zang (684900) |
| author2_role | author author author author author |
| author_facet | Wenyuan Xu (2553028) Jianbo Xu (1824238) Yongcheng Ji (5628128) Guodong Li (1525873) Hao Li (31608) Zhen Zang (684900) |
| author_role | author |
| dc.creator.none.fl_str_mv | Wenyuan Xu (2553028) Jianbo Xu (1824238) Yongcheng Ji (5628128) Guodong Li (1525873) Hao Li (31608) Zhen Zang (684900) |
| dc.date.none.fl_str_mv | 2025-09-04T17:38:01Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0330218.g019 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/A_comparison_chart_of_the_detection_effect_/30055545 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biochemistry Biotechnology Cancer Space Science Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified spatial pyramid pooling efficiently extracts multi detrimental gradients produced complex background interference 50 &# 8211 8 %, respectively target scale variability stage partial convolution stage partial channel insufficient detection accuracy improved yolov8n algorithm benchmark model yolov8n 8 %, 1 stage partial detection accuracy enhanced yolov8n 7 %, scale features enhanced algorithm algorithm introduces xlink "> weighted intersection significantly enhanced road fractures quality pictures neck paradigm model parameters loss function learning capability leakage due fine slim feature fusion experimental outcomes employed instead effectively reducing effectively accommodate complete intersection average precision attention mechanism aforementioned enhancements 95 ), |
| dc.title.none.fl_str_mv | A comparison chart of the detection effect. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <div><p>The improved YOLOv8n algorithm is proposed for the existing target detection algorithms to solve the issues of insufficient detection accuracy and leakage due to the target scale variability and complex background interference during road surface crack detection. This algorithm introduces the convolutional block attention module (CBAM) attention mechanism and integrates it with the cross-stage partial-feature fusion (C2f) module in the backbone network. The spatial pyramid pooling faster cross-stage partial channel (SPPFCSPC) module is introduced by integrating the spatial pyramid pooling (SPP) module with the Fully Cross-Stage Partial Convolution (FCSPC) module, which efficiently extracts multi-scale features. Then, the fine Slim-Neck paradigm is adopted to enhance the learning capability of the model while effectively reducing the number of model parameters. Ultimately, to mitigate the detrimental gradients produced by low-quality pictures, the weighted intersection over union (WIOU) loss function is employed instead of the complete intersection over union (CIOU), hence augmenting the bounding box regression efficacy of the network. After the aforementioned enhancements, the experimental outcomes on the road apparent crack dataset indicate that in comparison to the benchmark model YOLOv8n, the average precision (mAP@50), mean average precision (mAP@50–95), and recall of the enhanced algorithm have risen by 1.8%, 1.7%, and 1.8%, respectively. This indicates that the detection accuracy of road fractures is significantly enhanced by the enhanced YOLOv8n, which can more effectively accommodate the requirements of road maintenance.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_bc919444fea042fa76321dd4616b2ec9 |
| identifier_str_mv | 10.1371/journal.pone.0330218.g019 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30055545 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | A comparison chart of the detection effect.Wenyuan Xu (2553028)Jianbo Xu (1824238)Yongcheng Ji (5628128)Guodong Li (1525873)Hao Li (31608)Zhen Zang (684900)BiochemistryBiotechnologyCancerSpace ScienceBiological Sciences not elsewhere classifiedChemical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedspatial pyramid poolingefficiently extracts multidetrimental gradients producedcomplex background interference50 &# 82118 %, respectivelytarget scale variabilitystage partial convolutionstage partial channelinsufficient detection accuracyimproved yolov8n algorithmbenchmark model yolov8n8 %, 1stage partialdetection accuracyenhanced yolov8n7 %,scale featuresenhanced algorithmalgorithm introducesxlink ">weighted intersectionsignificantly enhancedroad fracturesquality picturesneck paradigmmodel parametersloss functionlearning capabilityleakage duefine slimfeature fusionexperimental outcomesemployed insteadeffectively reducingeffectively accommodatecomplete intersectionaverage precisionattention mechanismaforementioned enhancements95 ),<div><p>The improved YOLOv8n algorithm is proposed for the existing target detection algorithms to solve the issues of insufficient detection accuracy and leakage due to the target scale variability and complex background interference during road surface crack detection. This algorithm introduces the convolutional block attention module (CBAM) attention mechanism and integrates it with the cross-stage partial-feature fusion (C2f) module in the backbone network. The spatial pyramid pooling faster cross-stage partial channel (SPPFCSPC) module is introduced by integrating the spatial pyramid pooling (SPP) module with the Fully Cross-Stage Partial Convolution (FCSPC) module, which efficiently extracts multi-scale features. Then, the fine Slim-Neck paradigm is adopted to enhance the learning capability of the model while effectively reducing the number of model parameters. Ultimately, to mitigate the detrimental gradients produced by low-quality pictures, the weighted intersection over union (WIOU) loss function is employed instead of the complete intersection over union (CIOU), hence augmenting the bounding box regression efficacy of the network. After the aforementioned enhancements, the experimental outcomes on the road apparent crack dataset indicate that in comparison to the benchmark model YOLOv8n, the average precision (mAP@50), mean average precision (mAP@50–95), and recall of the enhanced algorithm have risen by 1.8%, 1.7%, and 1.8%, respectively. This indicates that the detection accuracy of road fractures is significantly enhanced by the enhanced YOLOv8n, which can more effectively accommodate the requirements of road maintenance.</p></div>2025-09-04T17:38:01ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0330218.g019https://figshare.com/articles/figure/A_comparison_chart_of_the_detection_effect_/30055545CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/300555452025-09-04T17:38:01Z |
| spellingShingle | A comparison chart of the detection effect. Wenyuan Xu (2553028) Biochemistry Biotechnology Cancer Space Science Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified spatial pyramid pooling efficiently extracts multi detrimental gradients produced complex background interference 50 &# 8211 8 %, respectively target scale variability stage partial convolution stage partial channel insufficient detection accuracy improved yolov8n algorithm benchmark model yolov8n 8 %, 1 stage partial detection accuracy enhanced yolov8n 7 %, scale features enhanced algorithm algorithm introduces xlink "> weighted intersection significantly enhanced road fractures quality pictures neck paradigm model parameters loss function learning capability leakage due fine slim feature fusion experimental outcomes employed instead effectively reducing effectively accommodate complete intersection average precision attention mechanism aforementioned enhancements 95 ), |
| status_str | publishedVersion |
| title | A comparison chart of the detection effect. |
| title_full | A comparison chart of the detection effect. |
| title_fullStr | A comparison chart of the detection effect. |
| title_full_unstemmed | A comparison chart of the detection effect. |
| title_short | A comparison chart of the detection effect. |
| title_sort | A comparison chart of the detection effect. |
| topic | Biochemistry Biotechnology Cancer Space Science Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified spatial pyramid pooling efficiently extracts multi detrimental gradients produced complex background interference 50 &# 8211 8 %, respectively target scale variability stage partial convolution stage partial channel insufficient detection accuracy improved yolov8n algorithm benchmark model yolov8n 8 %, 1 stage partial detection accuracy enhanced yolov8n 7 %, scale features enhanced algorithm algorithm introduces xlink "> weighted intersection significantly enhanced road fractures quality pictures neck paradigm model parameters loss function learning capability leakage due fine slim feature fusion experimental outcomes employed instead effectively reducing effectively accommodate complete intersection average precision attention mechanism aforementioned enhancements 95 ), |