Bacterial colonies detecting and counting based on enhanced CNN detection method

Shousheng Liu; Zhigang Gai; Xu Chai; Fengxiang Guo; Mei Zhang; Shanshan Xu; Yibao Wang; Ding Hu; Shaoyan Wang; Lili Zhang; Xueyu Zhang; Zhigang Chen; Xiaoling Sun; Xin Jiang

doi:10.1051/e3sconf/202123302012

All issues

Volume 233 (2021)

E3S Web Conf., 233 (2021) 02012

References

Open Access

Issue		E3S Web Conf. Volume 233, 2021 2020 2^nd International Academic Exchange Conference on Science and Technology Innovation (IAECST 2020)


Article Number		02012
Number of page(s)		4
Section		BFS2020-Biotechnology and Food Science
DOI		https://doi.org/10.1051/e3sconf/202123302012
Published online		27 January 2021

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[1] DALAL N, TRIGGS B. “Histograms of Oriented Gradients for Human Detection”, Proc. of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2005: 886-893 [Google Scholar]

[2] Ren S , He K , Girshick R , et al. “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks”. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2017, 39(6):1137-1149 [CrossRef] [Google Scholar]

[3] LIU W, ANGUELOV D, ERHAN D, et al. “SSD: Single Shot Multibox Detector”. Proc. of the 14th European Conference on Computer Vision. New York, USA: Springer, 2016, I: 21-37 [Google Scholar]

[4] Shousheng Liu, Zhigang Gai, Lin Cao, Hui Li, Ding Hu, Fengxiang Guo, and Huimin Qiu “Research on automatic detection method of infrared marine buoy based on CNN”, Proc. SPIE 11427, Second Target Recognition and Artificial Intelligence Summit Forum, 2020 [Google Scholar]

[5] Howard A G, Zhu M, Chen B, et al. “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications”[J]. arXiv: Computer Vision and Pattern Recognition, 2017 [Google Scholar]

[6] Ortega L, Rueda A. Parallel drainage network computation on CUDA[J]. Computers & Geoences, 2010, 36(2):171-178 [Google Scholar]

[7] Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev and etc. Caffe: Convolutional Architecture for Fast Feature Embedding. In Proceedings of the 22nd ACM international conference on Multimedia (MM ’14). NY, USA, 2014: 675–678 [Google Scholar]