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 720x1280 shaped images, it achieved 97.1% classification accuracy on 1863 toad and 2892 not-toad test images, which were not used in training.
Cite
@article{arxiv.1906.03547,
title = {In Situ Cane Toad Recognition},
author = {Dmitry A. Konovalov and Simindokht Jahangard and Lin Schwarzkopf},
journal= {arXiv preprint arXiv:1906.03547},
year = {2019}
}
Comments
Accepted for DICTA2018 https://doi.org/10.1109/DICTA.2018.8615780