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.

Impact and interest:

1 citations in Scopus
Search Google Scholar™
1 citations in Web of Science®

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

106 since deposited on 26 Oct 2015
59 in the past twelve months

Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

ID Code: 89491
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
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

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page