Lepidoptera Classification through Deep Learning

Autor:	Richard O. Sinnott, Guoen Jin, Xueting Tan, Xiaotian Jia
Rok vydání:	2020
Předmět:	Contextual image classification business.industry Interface (Java) Computer science Deep learning Single shot Machine learning computer.software_genre Convolutional neural network Information engineering Artificial intelligence Single phase business computer
Zdroj:	2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE).
DOI:	10.1109/csde50874.2020.9411382
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::096d00c7de18b723eefc1be615fe4d68 https://doi.org/10.1109/csde50874.2020.9411382 Zobrazit plný text záznamu