Detection of Bird Species by Image Processing With the Help of Deep Learning

Abstract

These days, countless species of birds are hardly found, besides it is problematic to categorize bird species once found. For instance, for diverse circumstances, birds come with altered dimensions, forms, shades, and as of an anthropoid lookout with diverse viewpoints. Certainly, the pictures display changed alterations that need to be noted by means of image recognition of bird classes. It is similarly tranquil for individuals en route for classifying birds in the images. In this paper, we were able to detect almost all kinds of bird species that are available by means of our dataset and deep learning networks. We collected the dataset from Kaggle which contains 30,000 data. We added a few more locally for more accuracy to be found. Detecting, learning, and studying bird species is easy with the help of images, that’s what we aimed for in this paper to make it easy and accurate. We applied convolutional neural networks (CNN), recurrent neural networks (RNN), and artificial neural networks (ANN) to find the best result. One of the significant prospects of the work is that while the image is processed for the detection of species of the bird throughout the dataset, it searches the whole and shows the matched result if found. But If the image is not matched with any of the images used in the dataset then it shows the best closest related species in spite of not showing anything. As our project motivates the study purpose so we aim to give a result either the matched one or learning about a new species that are related to the image given. Now among the applied algorithms, we have found that the convolutional neural networks (CNN) have performed better than the other two by giving an accuracy of approximately 98%.