A New Efficient Classifier for Bird Classification Based on Transfer Learning

Abstract

The problem of classification of different species of birds in the images is relevant in the modern world. The essence is to automatically determine the species of birds depicted in the photo using artificial intelligence. It is important for several areas of human life, in particular for nature conservation, environmental research, education, and ecotourism. The development of a classifier, based on deep learning methods, can help to effectively solve the problem of classifying different species of birds, ensuring high accuracy. The work developed a new effective algorithm for the classification of different species of birds in the images. An optimal model architecture was built using the transfer learning approach. A dataset of 525 bird species was analyzed and preprocessed in detail. The model training process was carried out, which includes two phases: only the upper layers are deactivated and the last 92 layers of the pretrained EfficientNetB5 model (not including BatchNormalization layers) and the top layers are activated. As a result, efficacy indicators were obtained: accuracy = 98.86%, precision = 0.99, recall = 0.99, and F1 score = 0.99, which showed improvement in comparison with modern research. The developed classifier is best suited for such areas of human life as education and ecotourism because for them the number of different species of birds that the classifier can determine is very important.