Detecting the birds and marine mammals from aerial images allows to monitor the evolution of their populations over time. As this is a tedious task, when done manually, reliable automatic methods using artificial intelligence are highly desired. This task differs from many standard object detection methods due to the high resolution of images (18 megapixels for the considered dataset) and small size of the animals (some are less than 50 square pixels). Also, changing waves and reflections on the water increase the difficulty of the task.
This thesis will focus on two main points. First, train, evaluate, and compare some standard object detection methods, such as Faster-RCNN. Second, replicate the method presented in “POLO – Point-based, multi-class animal detection”, and evaluate its performance on the considered dataset. The evaluations will also include some analysis of eventual links between accuracy and image quality (e.g., image luminosity or amount of waves). If time allows for it, tracking animals over multiple frames will be attempted.