Snake Detection and Classification using Deep Learning

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dc.contributor.authorYang, Zihan
dc.contributor.authorSinnott, Richard
dc.date.accessioned2020-12-24T19:13:40Z
dc.date.available2020-12-24T19:13:40Z
dc.date.issued2021-01-05
dc.description.abstractObject detection is a major task in computer vision. With the rapid development of machine learning in the past few decades, and more recently deep learning, it is now possible to utilise complex machine learning models to automatically detect and classify objects from potentially complex images. In this paper we consider machine (deep) learning networks suitable for detection and classification of (Australian) snakes and their deployment and performance in a mobile environment. We explore state of the art Convolutional Neural Networks (CNNs) and their use for transfer learning. We develop an iOS application supporting an offline (model-embedded on the device) approach and an online version where images are sent to a Cloud-based server for classification. We present the results and discuss the performance differences as well as the impact on the accuracy and time for classification for the two environments.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2021.148
dc.identifier.isbn978-0-9981331-4-0
dc.identifier.urihttp://hdl.handle.net/10125/70760
dc.language.isoEnglish
dc.relation.ispartofProceedings of the 54th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectDigital Mobile Services for Everyday Life
dc.subjectconvolutional neural network
dc.subjectdeep learning
dc.subjectimage classification
dc.subjectmobile computing
dc.titleSnake Detection and Classification using Deep Learning
prism.startingpage1212

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