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.
Introduction
Background of Study
The task of identifying and recognition of animals from photos has long been standing as there is no unique method that provides a robust and efficient solution to all situations.
Several researchers used long-standing traditional approaches for its implementation with the problem still hanging in limbo as the task hugely involve collecting a large volume of images which predominantly is conducted manually
with possibly images having an imperfect quality which sometimes affect the speed of classification, accuracy even for domain experts.
More so, processing these image sets is time-consuming, effort demanding, and comes at a very high cost as it is an overwhelming amount of data that is collected.
In recent years, much attention has focused on using deep neural network based techniques in the area of image processing, particularly animal recognition and identification.
However, the increase in the performance characteristics of the network depends on how scalable the network is designed.
References
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