Classification methods of butterfly images based on U-net and STL-MSDNet
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-023-14965-2.pdf
Reference35 articles.
1. Chen Y, Feng F, Yuan ZM (2011) Automatic identification of butterfly species with an improved support vector classification. Acta Entomol Sin 54:609–614. https://doi.org/10.16380/j.kcxb.2011.05.017
2. Dai J, Li Y, He K et al (2016) R-fcn: Object detection via region-based fully convolutional networks Advances in neural information processing systems, 29. https://arxiv.org/abs/1605.06409. Accessed 3 Dec 2020
3. Dong S, Wang P, Abbas K (2021) A survey on deep learning and its applications. Comput Scie Rev 40:100379. https://doi.org/10.1016/j.cosrev.2021.100379
4. Fan L (2015) The research on automatic identification of butterfly species based on the digital image. Master Dissertation. Beijing Forestry University, China https://kns.cnki.net/KCMS/detail/detail.aspx?dbname=CDFDLAST2015&filename=1015319461.nh. Accessed 13 Nov 2020
5. Girshick R (2015) Fast r-cnn. Proceedings of the IEEE international conference on computer vision, 1440-1448. https://doi.org/10.1109/ICCV.2015.169.
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