A Proposal of an Animal Detection System Using Machine Learning

One of the current challenges is to reduce collisions between vehicles and animals on roads, such accidents resulting in environmental imbalance and large expenditures in public coffers. This paper presents the components of a simple animal detection system and also a methodology for animals detection in images provided by cameras installed on the roads. This methodology allows the features extraction of regions of the image and the use of Machine Learning (ML) techniques to classify the areas into two classes: animal and non-animal. Two ML techniques were compared using synthetic images, traversing the pixels of the image using five distinctive approaches. Results show that the KNN learning model is more reliable than Random Forest to identify animals on roads accurately.