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Deep Learning Models for Waterfowl Detection and Classification in Aerial Images †.

Authors :
Zhang, Yang
Feng, Yuan
Wang, Shiqi
Tang, Zhicheng
Zhai, Zhenduo
Viegut, Reid
Webb, Lisa
Raedeke, Andrew
Shang, Yi
Source :
Information (2078-2489); Mar2024, Vol. 15 Issue 3, p157, 13p
Publication Year :
2024

Abstract

Waterfowl populations monitoring is essential for wetland conservation. Lately, deep learning techniques have shown promising advancements in detecting waterfowl in aerial images. In this paper, we present performance evaluation of several popular supervised and semi-supervised deep learning models for waterfowl detection in aerial images using four new image datasets containing 197,642 annotations. The best-performing model, Faster R-CNN, achieved 95.38% accuracy in terms of mAP. Semi-supervised learning models outperformed supervised models when the same amount of labeled data was used for training. Additionally, we present performance evaluation of several deep learning models on waterfowl classifications on aerial images using a new real-bird classification dataset consisting of 6,986 examples and a new decoy classification dataset consisting of about 10,000 examples per category of 20 categories. The best model achieved accuracy of 91.58% on the decoy dataset and 82.88% on the real-bird dataset. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20782489
Volume :
15
Issue :
3
Database :
Complementary Index
Journal :
Information (2078-2489)
Publication Type :
Academic Journal
Accession number :
176334030
Full Text :
https://doi.org/10.3390/info15030157