Recognition of Endemic Bird Species Using Deep Learning Models

Abstract

Numerous bird species have become extinct because of anthropogenic activities and climate change. The destruction of habitats at a rapid pace is a significant threat to biodiversity worldwide. Thus, monitoring the distribution of species and identifying the elements that make up the biodiversity of a region are essential for designing conservation stratagems. However, identifying bird species from images is a complicated and tedious task owing to interclass similarities and fine-grained features. To overcome this, in this study, we developed a transfer learning-based method using Inception-ResNet-v2 to detect and classify bird species endemic to Taiwan and to distinguish them from other object domains. Furthermore, to validate the reliability of the model, we adopted a technique that involves swapping misclassified data between training and validation datasets. The swapped data are retrained until the most suitable result is obtained. Additionally, fivefold cross-validation was performed to verify the predictive performance of the model. The proposed model was tested using 760 images of birds belonging to 29 species that are endemic to Taiwan; the images were captured from various environments with different perspectives and occlusions. Our model achieved an accuracy of 98.39% in the classification of the bird species and 100% in the detection of birds among different object categories. Moreover, the model achieved a precision, recall, and F1-score of 98.49%, 97.50%, and 97.90%, respectively, in classifying bird species endemic to Taiwan.