Construction of a bird image dataset for ecological investigations

Wind turbines have become significant collision risks to endangered birds. To estimate the impact of bird strikes and take action against them, we need an automated bird monitoring system that detects and categorizes bird species. Towards this goal, we constructed an image dataset as a benchmark for ecological investigations of birds. The dataset consists of time-series images of a scene in a wind farm, bounding boxes around the aerial objects in the images, and species annotations. Bird experts searched for and annotated the images of birds, and thus, even birds that appeared to be very small in the whole image could be specified in detail. In total, 32,000 birds are annotated. In collecting the images, we had to monitor birds in the distance to cover enough area, but even with a telephoto setup, the average size of the birds in the image is around 25 pixels. This means we need a means of recognition that works on very low-resolution images. As an application of the dataset, we conducted experiments in two-class categorization (birds and non-birds), to serve as basis for detection and further categorization. We found that the dataset is a challenging recognition task because of its low resolution and hard negative samples.