An Optimized YOLOv11 Framework for the Efficient Multi-Category Defect Detection of Concrete Surface
An Optimized YOLOv11 Framework for the Efficient Multi-Category Defect Detection of Concrete Surface
About this item
Full title
Author / Creator
Tian, Zhuang , Yang, Fan , Yang, Lei , Wu, Yunjie , Chen, Jiaying and Qian, Peng
Publisher
Switzerland: MDPI AG
Journal title
Language
English
Formats
Publication information
Publisher
Switzerland: MDPI AG
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Scope and Contents
Contents
Thoroughly and accurately identifying various defects on concrete surfaces is crucial to ensure structural safety and prolong service life. However, in actual engineering inspections, the varying shapes and complexities of concrete structural defects challenge the insufficient robustness and generalization of mainstream models, often leading to mis...
Alternative Titles
Full title
An Optimized YOLOv11 Framework for the Efficient Multi-Category Defect Detection of Concrete Surface
Authors, Artists and Contributors
Author / Creator
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Primary Identifiers
Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_ee08ccf926db4d2fb0d6621ba5c16959
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_ee08ccf926db4d2fb0d6621ba5c16959
Other Identifiers
ISSN
1424-8220
E-ISSN
1424-8220
DOI
10.3390/s25051291
