Zhang, Y., Y. Feng, S. Wang, Z. Tang, Z. Zhai, R. Viegut, E. Webb, A. Raedeke and Y. Shang. Deep Learning Models for Waterfowl Detection and Classification in Aerial Images. Information
Abstract
Waterfowl populations monitoring is essential for wetland conservation. Lately, deep 1 learning techniques have shown promising advancements in detecting waterfowl in aerial images. In 2 this paper, we present performance evaluation of several SOTA supervised and semi-supervised deep 3 learning models for waterfowl detection in aerial images using four new image datasets containing 4 197,642 annotations. The best-performing model, Faster R-CNN, achieved 95.38% accuracy in terms 5 of mAP. Semi-supervised learning models outperformed supervised models when the same amount 6 of labeled data were used for training. Additionally, we present performance evaluation of several 7 deep learning models on waterfowl classifications on aerial images using a new real-bird classification 8 dataset consisting of 6,986 examples and a new decoy classification dataset consisting of about 10,000 9 examples per category of 20 categories. The best model achieved accuracy of 91.58 % on the decoy 10 dataset and 82.88% on the real-bird dataset.