Snake Detection and Classification using Deep Learning

Yang, Zihan
Sinnott, Richard
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Object 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.
Digital Mobile Services for Everyday Life, convolutional neural network, deep learning, image classification, mobile computing
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