Experimental environment.
<div><p>To improve the accuracy and efficiency of crack segmentation in ancient wooden structures, we propose a lightweight deep neural network architecture, termed SMG-Net. The core innovation of this model lies in its multi-cooperative perception mechanism. First, the proposed Structur...
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2025
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| _version_ | 1852014704409444352 |
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| author | Tianke Fang (22648035) |
| author2 | Zhenxing Hui (22648038) Zhiying Xie (6339833) Peng Yu (48605) Yi Gao (112914) Songdi Shi (22648041) Yuanrong He (22648044) |
| author2_role | author author author author author author |
| author_facet | Tianke Fang (22648035) Zhenxing Hui (22648038) Zhiying Xie (6339833) Peng Yu (48605) Yi Gao (112914) Songdi Shi (22648041) Yuanrong He (22648044) |
| author_role | author |
| dc.creator.none.fl_str_mv | Tianke Fang (22648035) Zhenxing Hui (22648038) Zhiying Xie (6339833) Peng Yu (48605) Yi Gao (112914) Songdi Shi (22648041) Yuanrong He (22648044) |
| dc.date.none.fl_str_mv | 2025-11-19T18:35:17Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0336125.t007 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Experimental_environment_/30659322 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biochemistry Ecology Science Policy Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified statistical significance testing range feature dependencies made publicly available limited computational resources lightweight modular architecture heritage monitoring scenarios global structural consistency cooperative perception mechanism ancient wooden structures ancient wooden components 99 %, respectively div >< p grained crack segmentation fine crack details crack segmentation thereby improving source code shallow features scale features results confirmed proposed structure promote reproducibility particularly suitable multiple orientations module enhances inference speed future research extensive experiments establishes long discriminative capability directional pooling core innovation constructed dataset complex directions coherent recognition blurred edges aware cross 91 %, |
| dc.title.none.fl_str_mv | Experimental environment. |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <div><p>To improve the accuracy and efficiency of crack segmentation in ancient wooden structures, we propose a lightweight deep neural network architecture, termed SMG-Net. The core innovation of this model lies in its multi-cooperative perception mechanism. First, the proposed Structure-Aware Cross-directional Pooling (SACP) establishes long-range feature dependencies in multiple orientations, addressing the challenge of coherent recognition for cracks with complex directions. Second, the Multi-path Robust Feature Extraction (MRFE) module enhances the tolerance of the model to noise and blurred edges, thereby improving the discriminative capability of shallow features. Third, the Guided Semantic–Spatial Fusion (GSSFusion) mechanism enables efficient alignment and integration of multi-scale features, ensuring both fine crack details and global structural consistency in segmentation. Extensive experiments were conducted on a self-constructed dataset of cracks in ancient wooden components and the public Masonry crack dataset. SMG-Net achieved mean Intersection-over-Union (mIoU) scores of 81.12% and 87.91%, and Pixel Accuracy (PA) of 98.91% and 98.99%, respectively, significantly outperforming mainstream approaches such as U-Net, SegFormer, and Swin-UNet, with results confirmed by statistical significance testing. Moreover, SMG-Net demonstrates superior parameter efficiency and inference speed, making it particularly suitable for heritage monitoring scenarios with limited computational resources. To promote reproducibility and future research, the source code and datasets have been made publicly available at: <a href="https://github.com/HuiZhenxing/HuiZhenxing.git" target="_blank">https://github.com/HuiZhenxing/HuiZhenxing.git</a>.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_7ee680cfa280b545fcd08afc5f2eeec9 |
| identifier_str_mv | 10.1371/journal.pone.0336125.t007 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30659322 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Experimental environment.Tianke Fang (22648035)Zhenxing Hui (22648038)Zhiying Xie (6339833)Peng Yu (48605)Yi Gao (112914)Songdi Shi (22648041)Yuanrong He (22648044)BiochemistryEcologyScience PolicySpace ScienceEnvironmental Sciences not elsewhere classifiedBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedstatistical significance testingrange feature dependenciesmade publicly availablelimited computational resourceslightweight modular architectureheritage monitoring scenariosglobal structural consistencycooperative perception mechanismancient wooden structuresancient wooden components99 %, respectivelydiv >< pgrained crack segmentationfine crack detailscrack segmentationthereby improvingsource codeshallow featuresscale featuresresults confirmedproposed structurepromote reproducibilityparticularly suitablemultiple orientationsmodule enhancesinference speedfuture researchextensive experimentsestablishes longdiscriminative capabilitydirectional poolingcore innovationconstructed datasetcomplex directionscoherent recognitionblurred edgesaware cross91 %,<div><p>To improve the accuracy and efficiency of crack segmentation in ancient wooden structures, we propose a lightweight deep neural network architecture, termed SMG-Net. The core innovation of this model lies in its multi-cooperative perception mechanism. First, the proposed Structure-Aware Cross-directional Pooling (SACP) establishes long-range feature dependencies in multiple orientations, addressing the challenge of coherent recognition for cracks with complex directions. Second, the Multi-path Robust Feature Extraction (MRFE) module enhances the tolerance of the model to noise and blurred edges, thereby improving the discriminative capability of shallow features. Third, the Guided Semantic–Spatial Fusion (GSSFusion) mechanism enables efficient alignment and integration of multi-scale features, ensuring both fine crack details and global structural consistency in segmentation. Extensive experiments were conducted on a self-constructed dataset of cracks in ancient wooden components and the public Masonry crack dataset. SMG-Net achieved mean Intersection-over-Union (mIoU) scores of 81.12% and 87.91%, and Pixel Accuracy (PA) of 98.91% and 98.99%, respectively, significantly outperforming mainstream approaches such as U-Net, SegFormer, and Swin-UNet, with results confirmed by statistical significance testing. Moreover, SMG-Net demonstrates superior parameter efficiency and inference speed, making it particularly suitable for heritage monitoring scenarios with limited computational resources. To promote reproducibility and future research, the source code and datasets have been made publicly available at: <a href="https://github.com/HuiZhenxing/HuiZhenxing.git" target="_blank">https://github.com/HuiZhenxing/HuiZhenxing.git</a>.</p></div>2025-11-19T18:35:17ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0336125.t007https://figshare.com/articles/dataset/Experimental_environment_/30659322CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/306593222025-11-19T18:35:17Z |
| spellingShingle | Experimental environment. Tianke Fang (22648035) Biochemistry Ecology Science Policy Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified statistical significance testing range feature dependencies made publicly available limited computational resources lightweight modular architecture heritage monitoring scenarios global structural consistency cooperative perception mechanism ancient wooden structures ancient wooden components 99 %, respectively div >< p grained crack segmentation fine crack details crack segmentation thereby improving source code shallow features scale features results confirmed proposed structure promote reproducibility particularly suitable multiple orientations module enhances inference speed future research extensive experiments establishes long discriminative capability directional pooling core innovation constructed dataset complex directions coherent recognition blurred edges aware cross 91 %, |
| status_str | publishedVersion |
| title | Experimental environment. |
| title_full | Experimental environment. |
| title_fullStr | Experimental environment. |
| title_full_unstemmed | Experimental environment. |
| title_short | Experimental environment. |
| title_sort | Experimental environment. |
| topic | Biochemistry Ecology Science Policy Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified statistical significance testing range feature dependencies made publicly available limited computational resources lightweight modular architecture heritage monitoring scenarios global structural consistency cooperative perception mechanism ancient wooden structures ancient wooden components 99 %, respectively div >< p grained crack segmentation fine crack details crack segmentation thereby improving source code shallow features scale features results confirmed proposed structure promote reproducibility particularly suitable multiple orientations module enhances inference speed future research extensive experiments establishes long discriminative capability directional pooling core innovation constructed dataset complex directions coherent recognition blurred edges aware cross 91 %, |