Unsupervised Segmentation of Cattle Images Using Deep Learning

• Vinícius Guardieiro Sousa UFU
• André R. Backes UFU

DOI: https://doi.org/10.5753/wvc.2021.18886

Resumo

In this work, we used the Deep Learning (DL) architecture named U-Net to segment images containing side view cattle. We evaluated the ability of the U-Net to segment images captured with different backgrounds and from the different breeds, both acquired by us and from the Internet. Since cattle images present a more constant background than other applications, we also evaluated the performance of the U-Net when we change the numbers of convolutional blocks and filters. Results show that U-Net can be used to segment cattle images using fewer blocks and filters than traditional U-Net and that the number of blocks is more important than the total number of filters used.

Palavras-chave: U-Net, semantic segmentation, cow detection, deep Learning

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