Lepidoptera Classification through Deep Learning

Abstract

Deep learning for image recognition has received a lot of attention in recent years. In this paper we present a case study using two state-of-the-art deep learning libraries for image classification based on single phase (Single Shot Detection - SSD) and two-phase (Faster Region-based Convolutional Neural Network – Faster-RCNN) deep learning technologies. The case study is based on classification of lepidoptera: an order of species that includes butterflies and moths. We describe the data that was collected that underpinned this work. We also present the results and discuss the challenges with the work. Finally, we outline the implementation of a mobile application used as the client interface to the final solution.