Object counting from aerial remote sensing images: application to
  wildlife and marine mammals
Tanya Singh Hugo Gangloff Minh-Tan Pham
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.