[1] Bracciale L, Catini A, Gentile G, et al. Delay tolerant wireless sensor network for animal monitoring: the pink iguana case[C]//Proceedings of international conference on applications in electronics pervading industry, environment and society. Cham: Springer, 2016: 18-26.
[2] 陈善安, 胡春鹤, 张军国, 等.基于无线传感器网络的野生动物图像监测系统设计[J].现代制造技术与装备, 2017(3): 64-66. doi:  10.3969/j.issn.1673-5587.2017.03.030

Chen S A, Hu C H, Zhang J G, et al. Design of wildlife image monitoring system based on wireless sensor networks[J]. Modern Manufacturing Technology and Equipment, 2017(3): 64-66. doi:  10.3969/j.issn.1673-5587.2017.03.030
[3] Oishi Y, Matsunaga T. Automatic detection of moving wild animals in airborne remote sensing images[C]//2010 IEEE international geoscience and remote sensing symposium. Honolulu: IEEE, 2010: 517-519.
[4] 曾陈颖.面向珍稀野生动物保护的图像监测与识别技术研究[D].北京: 北京林业大学, 2015.

Zeng C Y. Research of image monitoring and identification oriented to rare wild animals protection[D]. Beijing: Beijing Forestry University, 2015.
[5] Okafor E, Pawara P, Karaaba F, et al. Comparative study between deep learning and bag of visual words for wild-animal recognition[C]//2016 IEEE symposium series on computational intelligence (SSCI). Athens: IEEE, 2017: 1-9.
[6] Villa A G, Salazar A, Vargas F. Towards automatic wild animal monitoring: identification of animal species in camera-trap images using very deep convolutional neural networks[J]. Ecological Informatics, 2017, 41: 24-32. doi:  10.1016/j.ecoinf.2017.07.004
[7] Yu X Y, Wang J P, Kays R, et al. Automated identification of animal species in camera trap images[J]. EURASIP Journal on Image and Video Processing, 2013, 2013: 52. doi:  10.1186/1687-5281-2013-52
[8] 李力, 林懿伦, 王飞跃, 等.平行学习—机器学习的一个新型理论框架[J].自动化学报, 2017, 43(1): 1-8. doi:  10.3969/j.issn.1003-8930.2017.01.001

Li L, Lin Y L, Wang F Y, et al. Parallel learning: a new framework for machine learning[J]. Acta Automatica Sinica, 2017, 43(1): 1-8. doi:  10.3969/j.issn.1003-8930.2017.01.001
[9] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[C]//Proceedings of international conference on neural information processing systems. Lake Tahoe: Curran Associates Inc., 2012: 1097-1105.
[10] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[J]. Computer Science, 2014: 23-31. doi:  10.5121/csit.2014.41000
[11] Szegedy C, Liu W, Jia Y Q, et al. Going deeper with convolutions[C]//Proceedings of 2015 IEEE conference on computer vision and pattern recognition (CVPR). Boston: IEEE, 2015: 1-9.
[12] He K M, Zhang X Y, Ren S Q, et al. Deep residual learning for image recognition[C]//Proceedings of 2016 IEEE conference on computer vision and pattern recognition (CVPR). Las Vegas: IEEE, 2016: 770-778.
[13] Huang G, Liu Z, Van Der Maaten L, et al. Densely connected convolutional networks[C]//Proceedings of 2017 IEEE conference on computer vision and pattern recognition. Honolulu: IEEE, 2017.
[14] Figueroa K, Camarena-Ibarrola A, García J, et al. Fast automatic detection of wildlife in images from trap cameras[M]//Bayro-Corrochano E, Hancock E. Progress in pattern recognition, image analysis, computer vision, and applications. Cham: Springer, 2014: 940-947.
[15] Chen G B, Han T X, He Z H, et al. Deep convolutional neural network based species recognition for wild animal monitoring[C]//Proceedings of 2014 IEEE international conference on image processing (ICIP). Paris: IEEE, 2015: 858-862.
[16] Yang W X, Jin L W, Tao D C, et al. DropSample: a new training method to enhance deep convolutional neural networks for large-scale unconstrained handwritten Chinese character recognition[J]. Pattern Recognition, 2016, 58: 190-203. doi:  10.1016/j.patcog.2016.04.007
[17] 王忠民, 曹洪江, 范琳.一种基于卷积神经网络深度学习的人体行为识别方法[J].计算机科学, 2016, 43(增刊2): 56-58, 87. http://d.old.wanfangdata.com.cn/Periodical/jsjkx2016z2012

Wang Z M, Cao H J, Fan L. Method on human activity recognition based on convolutional neural networks[J]. Computer Science, 2016, 43(Suppl.2): 56-58, 87. http://d.old.wanfangdata.com.cn/Periodical/jsjkx2016z2012
[18] 高震宇, 王安, 刘勇, 等.基于卷积神经网络的鲜茶叶智能分选系统研究[J].农业机械学报, 2017, 48(7): 53-58. http://d.old.wanfangdata.com.cn/Periodical/nyjxxb201707007

Gao Z Y, Wang A, Liu Y, et al. Intelligent fresh-tea-leaves sorting system research based on convolution neural network[J]. Transactions of the Chinese Society for Agricultural Machinery, 2017, 48(7): 53-58. http://d.old.wanfangdata.com.cn/Periodical/nyjxxb201707007
[19] 吴笑鑫, 高良, 闫民, 等.基于多特征融合的花卉种类识别研究[J].北京林业大学学报, 2017, 39(4): 86-93. doi:  10.13332/j.1000-1522.20160367

Wu X X, Gao L, Yan M, et al. Flower species recognition based on fusion of multiple features[J]. Journal of Beijing Forestry University, 2017, 39(4): 86-93. doi:  10.13332/j.1000-1522.20160367
[20] 那顺得力格尔.内蒙古赛罕乌拉国家级自然保护区陆生野生动物保护监测研究[D].北京: 北京林业大学, 2011.

Nasendeleger. Monitoring on wildlife biodiversity at Saihanwula National Nature Reserve[D]. Beijing: Beijing Forestry University, 2011.
[21] Russakovsky O, Deng J, Su H, et al. ImageNet large scale visual recognition challenge[J]. International Journal of Computer Vision, 2015, 115(3): 211-252. doi:  10.1007/s11263-015-0816-y
[22] 杨国国, 鲍一丹, 刘子毅.基于图像显著性分析与卷积神经网络的茶园害虫定位与识别[J].农业工程学报, 2017, 33(6): 156-162. http://d.old.wanfangdata.com.cn/Periodical/nygcxb201706020

Yang G G, Bao Y D, Liu Z Y. Localization and recognition of pests in tea plantation based on image saliency analysis and convolutional neural network[J]. Transactions of the Chinese Society of Agricultural Engineering, 2017, 33(6): 156-162. http://d.old.wanfangdata.com.cn/Periodical/nygcxb201706020
[23] 谢将剑, 李文彬, 张军国, 等.基于Chirplet语图特征和深度学习的鸟类物种识别方法[J].北京林业大学学报, 2018, 40(3): 122-127. doi:  10.13332/j.1000-1522.20180008

Xie J J, Li W B, Zhang J G, et al. Bird species recognition method based on Chirplet spectrogram feature and deep learning[J]. Journal of Beijing Forestry University, 2018, 40(3): 122-127. doi:  10.13332/j.1000-1522.20180008
[24] Girshick R. Fast R-CNN[C]//Proceedings of IEEE international conference on computer vision (ICCV). Santiago: IEEE, 2015: 1440-1448.