基于深度学习的野生动物监测与识别研究进展  

Research Progress of the Wildlife Monitoring and Identification Based on Deep Learning

作　　者：张雪莹[1] 张浩林[1] 韩莹莹[1] 翁强[1] 袁峥嵘[1] 姚远 ZHANG Xueying;ZHANG Haolin;HAN Yingying;WENG Qiang;YUAN Zhengrong;YAO Yuan(College of Biological Sciences and Technology,Beijing Forestry University,Beijing,100083,China;Institute of Automation,Chinese Academy of Science,Beijing,100190,China)

机构地区：[1]北京林业大学生物科学与技术学院,北京100083 [2]中国科学院自动化研究所,北京100190

出　　处：《野生动物学报》2022年第1期251-258,共8页CHINESE JOURNAL OF WILDLIFE

基　　金：北京林业大学国家级“大学生创新训练项目”(G202010022072)。

摘　　要：简要阐述了深度学习及常用网络模型,归纳了野生动物监测的发展进程与技术,从图像、视频、音频3个角度总结了近年来深度学习在物种识别及观测中的研究进展,对深度学习在野生动物保护与利用工作中存在的问题进行了分析与探讨,并对其未来发展趋势做出展望。This paper briefly described deep learning and common network models,and summarized the process and technology of wildlife monitoring development.In this paper,the research progress of deep learning in species identification and observation in recent years has been summarized from the perspectives of image,video and audio.Then,the problems existing in deep learning in wildlife conservation and utilization were analyzed and discussed.The future development trend of deep learning has been prospected.

关 键 词：深度学习 人工智能 野生动物识别 野生动物保护与利用 

分 类 号：Q958.1[生物学—动物学] TP391.4[自动化与计算机技术—计算机应用技术]