Two-Stage Classification Model for Feather Images Identification

Alina Belko, Konstantin Dobratulin, Andrey Kuznetsov

Abstract: The paper explores the usage of neural networks for bird species identification based on feathers image. The taxonomic identification of birds’ feather is widely used in aviation ornithology to analyze collisions with aircraft and develop methods to prevent them. This article presents a novel dataset consisting of 28,272 images of the plumage of 595 bird species. We compare models trained on four subsets from the initial dataset. We propose the method of identifying bird species based on YoloV4 and DenseNet models. The experimental estimation showed that the resulted method makes it possible to identify the bird based on the photograph of the single feather with an accuracy up to 81,03% for precise classification and with accuracy 97,09% for of the first five predictions of the classifier.