Pavement Disease Detection through Improved YOLOv5s Neural Network

An improved Ghost-YOLOv5s detection algorithm is proposed in this paper to solve the problems of high computational load and undesirable recognition rate in the traditional detection methods of pavement diseases. Ghost modules and C3Ghost are introduced into the YOLOv5s network to reduce the FLOPs (...

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Detalles Bibliográficos
Autores principales: Chu, Yinze, Xiang, Xinjian, Wang, Yilin, Huang, Binqiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9584716/
https://www.ncbi.nlm.nih.gov/pubmed/36275977
http://dx.doi.org/10.1155/2022/1969511

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9584716/
https://www.ncbi.nlm.nih.gov/pubmed/36275977
http://dx.doi.org/10.1155/2022/1969511

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