Convolutional Neural Network for Detecting Endangered Exotic Birds
DOI:
https://doi.org/10.26439/ciis2019.5513Keywords:
object recognition, convolutional neural networks, wildlife tradeAbstract
In the last years, the rates of illegal wildlife trade have increased due to the lack of awareness for caring and preserving our ecosystem. Between the years 2000 and 2015, sixtyseven thousand seven hundred forty-nine (67,749) wild animals were illegally taken from Peru, including 29,591 exotic birds (43%), 26,951 amphibians (40%), 8,600 reptiles (13%) and 2,607 mammals (4%) valued at approximately USD 1,000. The proposal to significantly reduce these illegal acts is based on having an application that can identify the species attempted to be illegally traded. Such application will use the architectures of convolutional neural networks called VGGNet16, which will allow the correct identification of the animal. The present research used a database of real animals, obtaining an effectiveness of 89%.
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