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%.
