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Wildlife Monitoring and Identification based on Faster R-CNN

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成果类型:
会议论文
作者:
Chenxi Deng;Guoxiong Zhou;Yiqing Cai
作者机构:
[Chenxi Deng] Ecological Livable College, Hunan University of Environment and Biology, Hengyang, China
[Guoxiong Zhou; Yiqing Cai] School of Computer and Information Engineering, Central South University of Forestry & Technology, Changsha, China
语种:
英文
关键词:
Image preprocessing;Animal image detection;TensorFlow framework;Faster R-CNN
年:
2023
页码:
638-642
会议名称:
2023 International Conference on Advances in Electrical Engineering and Computer Applications (AEECA)
会议论文集名称:
2023 International Conference on Advances in Electrical Engineering and Computer Applications (AEECA)
会议时间:
18 August 2023
会议地点:
Dalian, China
出版者:
IEEE
ISBN:
979-8-3503-0809-9
机构署名:
本校为其他机构
院系归属:
计算机与信息工程学院
摘要:
China is a powerful country with vast territory and abundant animal resources. At present, there are more than 400 kinds of national protected animals, and there are 1999 man-made reserves. Wildlife resources have important strategic significance. Real time detection and identification of wildlife is the main work of managers. Based on the research background of wildlife image detection, this paper analyzes and summarizes the traditional image detection methods, and proposes a wildlife detection and automatic recognition method based on fast r-cnn. In this paper, the tensorflow framework is do...

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