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This document describes a study that uses machine learning models for automated animal detection and classification from surveillance camera images. Specifically, it compares the Region-based Convolutional Neural Network (R-CNN) classification model, Single Shot multibox Detection (SSD) regression model, and You Only Look Once (YOLO) regression model on a dataset of 2,000 images of elephants, cheetahs, cows and horses. The R-CNN model achieved 95.22% mean average precision, while SSD and YOLO achieved highe

Author

balakrishnak ece

Language

EN