Isfahan University of Medical Sciences

Science Communicator Platform

Stay connected! Follow us on X network (Twitter):
Share this content! On (X network) By
In Situ Cane Toad Recognition Publisher



Konovalov DA1 ; Jahangard S2 ; Schwarzkopf L1
Authors
Show Affiliations
Authors Affiliations
  1. 1. College of Science and Engineering, James Cook University, Townsville, Australia
  2. 2. Isfahan University of Medical Science, Isfahan, Iran

Source: 2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018 Published:2019


Abstract

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. © 2018 IEEE.
Related Docs
1. Retinal Optical Coherence Tomography Image Classification With Label Smoothing Generative Adversarial Network, Neurocomputing (2020)
2. Attention to Lesion: Lesion-Aware Convolutional Neural Network for Retinal Optical Coherence Tomography Image Classification, IEEE Transactions on Medical Imaging (2019)
3. Three-Dimensional Optical Coherence Tomography Image Denoising Through Multi-Input Fully-Convolutional Networks, Computers in Biology and Medicine (2019)
4. Automatic Classification of Retinal Optical Coherence Tomography Images With Layer Guided Convolutional Neural Network, IEEE Signal Processing Letters (2019)
5. Macular Oct Classification Using a Multi-Scale Convolutional Neural Network Ensemble, IEEE Transactions on Medical Imaging (2018)
6. Breast Cancer Diagnosis in Dce-Mri Using Mixture Ensemble of Convolutional Neural Networks, Pattern Recognition (2017)
7. Automatic Classification of Retinal Three-Dimensional Optical Coherence Tomography Images Using Principal Component Analysis Network With Composite Kernels, Journal of Biomedical Optics (2017)
Experts (# of related papers)
Hossein Rabbani (6)
Alireza Mehri-Dehnavi (1)