A Pipeline Defect Instance Segmentation System Based on SparseInst

Deep learning algorithms have achieved encouraging results for pipeline defect segmentation. However, existing defect segmentation methods may encounter challenges in accurately segmenting the complex features of pipeline defects and suffer from low processing speeds. Therefore, in this study, we pr...

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Detalles Bibliográficos
Autores principales: Wang, Niannian, Zhang, Jingzheng, Song, Xiaotian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10675068/
https://www.ncbi.nlm.nih.gov/pubmed/38005407
http://dx.doi.org/10.3390/s23229019

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10675068/
https://www.ncbi.nlm.nih.gov/pubmed/38005407
http://dx.doi.org/10.3390/s23229019

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