In situ cane toad recognition
Conference Publication ResearchOnline@JCUAbstract
Cane toads are invasive, toxic to native predators, compete with native insectivores, and have a devastating impact on Australian ecosystems, prompting the Australian government to list toads as a key threatening process under the Environment Protection and Biodiversity Conservation Act 1999. Mechanical cane toad traps could be made more native-fauna friendly if they could distinguish invasive cane toads from native species. Here we designed and trained a Convolution Neural Network (CNN) starting from the Xception CNN. The XToadGmp toad-recognition CNN we developed was trained end-to-end using heat-map Gaussian targets. After training, XToadGmp required minimum image pre/post-processing and when tested on 720×1280 shaped images, it achieved 97.1% classification accuracy on 1863 toad and 2892 not-toad test images, which were not used in training.
Journal
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Publication Name
2018 Digital Image Computing: Techniques and Applications (DICTA)
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ISBN/ISSN
978-1-5386-6602-9
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Pages Count
7
Location
Canberra, ACT, Australia
Publisher
Institute of Electrical and Electronics Engineers
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Publisher Location
Piscataway, NJ, USA
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Date
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DOI
10.1109/DICTA.2018.8615780