In Situ Cane Toad Recognition

9 Jun 2019 · Dmitry A. Konovalov, Simindokht Jahangard, Lin Schwarzkopf ·

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.

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General Classification

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1x1 Convolution • Average Pooling • Convolution • Dense Connections • Depthwise Convolution • Depthwise Separable Convolution • Global Average Pooling • Max Pooling • Pointwise Convolution • ReLU • Residual Connection • Softmax • Xception

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In Situ Cane Toad Recognition

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