Improved YOLOv3 Integrating SENet and Optimized GIoU Loss for Occluded Pedestrian Detection

Occluded pedestrian detection faces huge challenges. False positives and false negatives in crowd occlusion scenes will reduce the accuracy of occluded pedestrian detection. To overcome this problem, we proposed an improved you-only-look-once version 3 (YOLOv3) based on squeeze-and-excitation networ...

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Detalles Bibliográficos
Autores principales: Zhang, Qiangbo, Liu, Yunxiang, Zhang, Yu, Zong, Ming, Zhu, Jianlin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10675795/
https://www.ncbi.nlm.nih.gov/pubmed/38005475
http://dx.doi.org/10.3390/s23229089

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10675795/
https://www.ncbi.nlm.nih.gov/pubmed/38005475
http://dx.doi.org/10.3390/s23229089

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