A SURVEY AND ANALYSIS OF DEEP LEARNING TECHNIQUES FOR BIRD SPECIES CLASSIFICATION

Abstract

The ability to accurately identify the species of a bird in an image is crucial. A bird’s species identification can be accomplished using images and audios. In earlier periods, the audio of birds are utilized to possibly recognize the different species of birds. But, background noise from things like birds, insects, and the wind makes it difficult for this method to produce a reliable result. Comparatively, observer’s finds images are better than audios. Using images, people are better able to discriminate between birds. However, because of the inexperience of most bird watchers and the similarity of bird forms and backgrounds, identifying birds can be difficult. To address this, Deep Learning (DL) models have been implemented to efficiently extract features from photos collected for recognition. DL models for bird species identification provides more accuracy. The recently proposed transfer learning and spatial pyramid pooling efficiently classify bird spicies. Another recently proposed Mask-CNN based method solved few shot classifcation problem effectively. But, both of these method are suffered to distinguish the subcategory of spicies form main categories. In this article, the of bird species identification techniques are studied in brief to encourage further research in this field. First, the review is planned to investigate the DL algorithms for identifying the different bird species types. Next, the merits and demerits of every algorithms are analyzed based on its performance. Finally, potential improvements are emphasized to achieve greater efficiency in identifying the bird species.