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Animal Detection for Road safety using Deep Learning

The recognition of big animals on the images with road scenes has received little attention in modern research. There are very few specialized data sets for this task. Popular open data sets contain many images of big animals, but the most part of them is not correspond to road scenes that is necessary for on-board vision systems of unmanned vehicles. The paper describes the preparation of such a specialized data set based on Google Open Images and COCO datasets. The

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Applying computer-aided photo-identification to messy datasets: a case study of Thornicroft’s giraffe (Giraffa camelopardalis thornicrofti)

Digital photography enables researchers to rapidly compile large quantities of data from individually identifiable animals, and computer software improves the management of such large datasets while aiding the identification process. Wild-ID software has performed well with uniform datasets controlling for angle and portion of the animal photographed; however, few datasets are collected under such controlled conditions. We examined the effectiveness of Wild-ID in identifying individual Thornicroft’s giraffe from a dataset of photographs (n = 552) collected opportunistically in the Luangwa

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