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

页码：170 / 171

页数：2

共 50 条

[31] HIGH-SPEED SHIP DETECTION IN SAR IMAGES BY IMPROVED YOLOV3
Zhang, Tianwen
Zhang, Xiaoling
Shi, Jun
Wei, Shunjun
2019 16TH INTERNATIONAL COMPUTER CONFERENCE ON WAVELET ACTIVE MEDIA TECHNOLOGY AND INFORMATION PROCESSING (ICWAMTIP), 2019, : 149 - 152
[32] Small Target Detection for UAV Aerial Images Based on Improved YOLOv3
Liu, Yang
Zhao, Tongzhou
Shen, Zhiyu
2021 4TH INTERNATIONAL CONFERENCE ON ROBOTICS, CONTROL AND AUTOMATION ENGINEERING (RCAE 2021), 2021, : 12 - 16
[33] Vehicle Detection in the Aerial Infrared Images via an Improved Yolov3 Network
Zhang, Xunxun
Zhu, Xu
2019 IEEE 4TH INTERNATIONAL CONFERENCE ON SIGNAL AND IMAGE PROCESSING (ICSIP 2019), 2019, : 372 - 376
[34] Detection of white blood cells using YOLOV3 network
Gan Fang
Yang Suhua
Jiang Shaofeng
PROCEEDINGS OF 2019 14TH IEEE INTERNATIONAL CONFERENCE ON ELECTRONIC MEASUREMENT & INSTRUMENTS (ICEMI), 2019, : 1683 - 1688
[35] Litchi detection in the field using an improved YOLOv3 model
Peng, Hongxing
Xue, Chao
Shao, Yuanyuan
Chen, Keyin
Liu, Huanai
Xiong, Juntao
Chen, Hu
Gao, Zongmei
Yang, Zhengang
INTERNATIONAL JOURNAL OF AGRICULTURAL AND BIOLOGICAL ENGINEERING, 2022, 15 (02) : 211 - 220
[36] Rare plants detection using a YOLOv3 neural network
Gorodetskaya, Lyudmila Alexandrovna
Denisova, Anna Yurievna
Kavelenova, Lyudmila Mikhailovna
Fedoseev, Victor Andreevich
COMPUTER OPTICS, 2024, 48 (03) : 397 - 405
[37] Defect Detection of Texture Tile Using Improved YOLOv3
Li Zehui
Chen Xindu
Huang Jiasheng
Wu Lei
Lian Yangqi
LASER & OPTOELECTRONICS PROGRESS, 2022, 59 (10)
[38] Colon cancer stage detection in colonoscopy images using YOLOv3 MSF deep learning architecture
Murugesan, Malathi
Arieth, R. Madonna
Balraj, Shankarlal
Nirmala, R.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2023, 80
[39] Coal gangue detection and recognition algorithm based on deformable convolution YOLOv3
Li, De-yong
Wang, Guo-fa
Zhang, Yong
Wang, Shuang
IET IMAGE PROCESSING, 2022, 16 (01) : 134 - 144
[40] Application of Improved YOLOv3 Algorithm in Mask Recognition
Meng, Fanxing
Wei, Weimin
Cai, Zhi
Liu, Chang
INTELLIGENT INFORMATION AND DATABASE SYSTEMS, ACIIDS 2021, 2021, 12672 : 544 - 555

← 1 2 3 4 5 →