Wildlife Detection and Recognition in Digital Images Using YOLOv3

被引：2
|
作者：
Gabriel, Mina [1 ]
Cha, Sangwhan [1 ]
Al-Nakash, Nushwan Yousif B. [1 ]
Yun, Daqing [1 ]
机构：
[1] Harrisburg Univ Sci & Technol, Harrisburg, PA 17101 USA
来源：
2020 IEEE CLOUD SUMMIT | 2020年
关键词：
Deep learning; YOLOv3; wildlife detection and classification; digital images;
D O I：
10.1109/IEEECloudSummit48914.2020.00033
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Recent advances in hardware capability and machine learning techniques enable convenient monitoring of wildlife and their living environments. In this work, we apply Deep Learning (DL) methods to detect and recognize wildlife in digital images and report the experimental results conducted in a commodity workstation. Specifically, YOLOv3 and YOLOv3-Tiny are used to detect and classify several classes of animals based on 9051 digital images and they achieve 75.2% and 68.4% mean average precision, respectively.
引用
收藏
页码：170 / 171
页数：2
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