Anthropogenic activities pose threats to wildlife and marine fauna, prompting
the need for efficient animal counting methods. This research study utilizes
deep learning techniques to automate counting tasks. Inspired by previous
studies on crowd and animal counting, a UNet model with various backbones is
implemented, which uses Gaussian density maps for training, bypassing the need
of training a detector. The new model is applied to the task of counting
dolphins and elephants in aerial images. Quantitative evaluation shows
promising results, with the EfficientNet-B5 backbone achieving the best
performance for African elephants and the ResNet18 backbone for dolphins. The
model accurately locates animals despite complex image background conditions.
By leveraging artificial intelligence, this research contributes to wildlife
conservation efforts and enhances coexistence between humans and wildlife
through efficient object counting without detection from aerial remote sensing.

利用深度学习技术实现动物自动计数，实验表明，UNet 模型应用高斯密度图训练，以达到快速高效计数目的，特别适用于复杂背景下的航空图像中非洲象和海豚的计数任务，减少了人类对野生动物的干扰，为野生动物保护与和谐相处提供了有效的技术手段。