An iterative noisy annotation correction model for robust plant disease detection

Previous work on plant disease detection demonstrated that object detectors generally suffer from degraded training data, and annotations with noise may cause the training task to fail. Well-annotated datasets are therefore crucial to build a robust detector. However, a good label set generally requ...

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Detalles Bibliográficos
Autores principales: Dong, Jiuqing, Fuentes, Alvaro, Yoon, Sook, Kim, Hyongsuk, Park, Dong Sun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Plant Science
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10628849/
https://www.ncbi.nlm.nih.gov/pubmed/37941667
http://dx.doi.org/10.3389/fpls.2023.1238722

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10628849/
https://www.ncbi.nlm.nih.gov/pubmed/37941667
http://dx.doi.org/10.3389/fpls.2023.1238722

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