Study of Scalable Deep Neural Network for Wildlife Animal Recognition and Identification

Abstract

Recently, deep learning techniques have been used significantly for large scale image classification targeting wildlife prediction. This research adopted a deep convolutional neural network (CNN) and proposed a deep scalable CNN. Our research essentially modifies the network layers (scalability) dynamically in a multitasking system and enables real-time operations with minimum performance loss. It suggests a straightforward technique to access the performance gains of the network while enlarging the network layers. This is helpful as it reduces redundancy in network layers and boosts network efficiency. The architecture implementation was done in software using keras framework and tensorflow as the backend on the CPU and to corroborate the universality and robustness of our proposed approach; we train our model on a GPU with a newly created dataset named “Zedataset”, preprocessed for performance evaluation. Results obtained from our experimentations show that our proposed architecture design will perform better with more dataset at the set optimum parameters.