Automating marine mammal detection in aerial images captured during wildlife surveys: A deep learning approach
Maire, Frederic, Mejias Alvarez, Luis, & Hodgson, Amanda (2015) Automating marine mammal detection in aerial images captured during wildlife surveys: A deep learning approach. In Maher, Michael & Thiebaux, Sylvie (Eds.) 28th Australasian Joint Conference on Artificial Intelligence (AI 2015), 30 November – 4 December 2015, Canberra, A.C.T. (In Press)
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.
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|Item Type:||Conference Paper|
|Keywords:||Image processing, Deep learning, pattern recognition, wild life monitoring|
|Divisions:||Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright 2015 [Please consult the author]|
|Deposited On:||26 Oct 2015 22:55|
|Last Modified:||10 May 2016 07:17|
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