北京测绘2024，Vol.38Issue(9)：1237-1242,6.DOI:10.19580/j.cnki.1007-3000.2024.09.001
基于深度学习的野生动物图像识别研究综述
Review of deep learning-based wildlife image recognition studies
摘要
Abstract
The deepening of national attention to the construction of ecological civilization and the major breakthrough of computer ability provide a new opportunity for realizing more efficient and accurate wildlife image recognition.The deep learning(DL)technology based on computer vision has played a great advantage in the field of image recognition.The application of the DL algorithm to wildlife image recognition can capture more detailed and accurate wildlife information and thus better help managers identify and monitor wildlife and protect the ecological environment and species diversity.This paper started with two aspects of public datasets and field data acquisition,analyzed the research status of deep learning,and introduced the research progress of the DL algorithm in wildlife image recognition.The paper focused on the present situation of regional convolutional neural networks(R-CNNs)and YOLO algorithms,so as to provide a theoretical basis for more efficient wildlife image recognition and offer new ideas for image recognition.关键词
深度学习(DL)/卷积神经网络(CNN)/野生动物/图像识别Key words
deep learning(DL)/convolutional neural network(CNN)/wildlife/image recognition分类
天文与地球科学引用本文复制引用
杨拂晓,费龙,闫泰辰..基于深度学习的野生动物图像识别研究综述[J].北京测绘,2024,38(9):1237-1242,6.基金项目
吉林省科技发展计划(20230203001SF) （20230203001SF）
