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Marine vertebrate predator detection and recognition in underwater videos by region convolutional neural network

Citation

Park, M and Yang, W and Cao, Z and Kang, B and Connor, D and Lea, M-A, Marine vertebrate predator detection and recognition in underwater videos by region convolutional neural network, Lecture Note in Computer Science: Proceedings of the 16th Pacific Rim Knowledge Acquisition Workshop: Knowledge Management and Acquisition for Intelligent Systems (PKAW 2019), 26-27 August 2019, Cuvu, Fiji ISBN 978-3-030-30638-0 (2019) [Refereed Conference Paper]

Copyright Statement

Copyright 2019 Springer

Official URL: https://doi.org/10.1007/978-3-030-30639-7_7

Abstract

In this paper, we present R-CNN, Fast R-CNN and Faster R-CNN methods to automatically detect and recognise the predators in underwater videos. We compare the results of these methods on real data and discuss their strengths and weaknesses. We build a dataset using footage captured from representative environment of the wild and devise a data model with three classes (seal, dolphin, background). Following this, we train R-CNN, Fast R-CNN and Faster R-CNN, then evaluate them on a test dataset compose of challenging objects that had not been seen during training. We perform evaluation on GPU, acquiring information about the AP and IOU for each model and network based on various proposal numbers as well as runtime speeds. Based on the results, we found that the best model of predator detection using visual deep learning models is Faster R-CNN with 2000 proposals.

Item Details

Item Type:Refereed Conference Paper
Keywords:R-CNN, fast R-CNN, faster R-CNN, marine vertebrate, seal, dolphin, detection, recognition, deep learning
Research Division:Information and Computing Sciences
Research Group:Artificial Intelligence and Image Processing
Research Field:Computer Vision
Objective Division:Defence
Objective Group:Defence
Objective Field:Intelligence
UTAS Author:Park, M (Dr Mira Park)
UTAS Author:Yang, W (Ms Wenli Yang)
UTAS Author:Cao, Z (Mr Zehong Cao)
UTAS Author:Kang, B (Professor Byeong Kang)
UTAS Author:Lea, M-A (Associate Professor Mary-Anne Lea)
ID Code:135100
Year Published:2019
Deposited By:Information and Communication Technology
Deposited On:2019-09-29
Last Modified:2019-11-07
Downloads:0

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