Object counting from aerial remote sensing images: application to wildlife and marine mammals
This work addresses wildlife conservation by providing an efficient counting method for monitoring animal populations from aerial remote sensing, but it is incremental as it adapts existing techniques to new data.
The study tackled automated counting of elephants and dolphins in aerial images using a UNet model with Gaussian density maps, achieving the best performance with EfficientNet-B5 for elephants and ResNet18 for dolphins.
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.