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Automating marine mammal detection in aerial images captured during wildlife surveys: A deep learning approach

Maire, F., Alvarez, L.M. and Hodgson, A. (2015) Automating marine mammal detection in aerial images captured during wildlife surveys: A deep learning approach. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9457 . pp. 379-385.

Link to Published Version: http://dx.doi.org/10.1007/978-3-319-26350-2_33
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Abstract

Aerial surveys conducted using manned or unmanned aircraft with customized camera payloads can generate a large number of images. Manual review of these images to extract data is prohibitive in terms of time and financial resources, thus providing strong incentive to automate this process using computer vision systems. There are potential applications for these automated systems in areas such as surveillance and monitoring, precision agriculture, law enforcement, asset inspection, and wildlife assessment. In this paper, we present an efficient machine learning system for automating the detection of marine species in aerial imagery. The effectiveness of our approach can be credited to the combination of a well-suited region proposal method and the use of Deep Convolutional Neural Networks (DCNNs). In comparison to previous algorithms designed for the same purpose, we have been able to dramatically improve recall to more than 80% and improve precision to 27% by using DCNNs as the core approach.

Publication Type: Journal Article
Murdoch Affiliation: School of Veterinary and Life Sciences
Publisher: Springer
Copyright: © Springer International Publishing Switzerland 2015.
URI: http://researchrepository.murdoch.edu.au/id/eprint/29559
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