A hybrid framework for wild animal classification using fine-tuned DenseNet121 and machine learning classifiers

Abstract

Over the past few decades, wildlife monitoring has become an active research area. Various topics like animal-vehicle collision, human-animal conflict, animal poaching, population demography, and tracking of animal behaviour come under wildlife monitoring. Different methods have been used for wild animal monitoring, out of which machine learning (ML) and deep learning (DL) are widely used for automatic detection and classification of species from digital images. Both ML and DL have their advantages and disadvantages. A hybrid model has been proposed by integrating the advantage of DL and ML to detect and classify animals automatically. Publicly available datasets of five wild animals are used to train the model, and for testing the model, a dataset is created by capturing the images with the help of a camera and mobile device in different locations and under various environmental conditions. Two approaches are proposed using a hybrid model: a pre-trained dense convolution neural network 121 (DenseNet121) model is used in the first approach, and a finetuned DenseNet121 model is used in the second approach for feature extraction. Extracted features from the pre-trained and finetuned DenseNet121 model are fed into ML classifiers such as extreme gradient boosting (XGBoost), random forest (RF), and naïve Bayes (NB) for classification. When the performance was analysed, the second approach performed better than the first.