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Animal Detection for Road Safety Using Deep Learning

EasyChair Preprint no. 6666

9 pagesDate: September 23, 2021


Over the years, Accidents due to animals crossing the road at unexpected moments is still been a great cause of road death. Roads near the forest are dark and dense hence drivers are not able to spot the animals clear. Truck drivers face issues due to blindspot regions. In this paper, we are proposing a model that can efficiently detect the animals and alarm the driver. Using Machine learning - Deep learning algorithm we are segregating the animals with the help of a huge open-source dataset. Using convolution neural networks the model will learn by itself to predict the object for every frame of the image received from the live camera. If the machine marks object as an animal the system gives an alert of 3 seconds to make the driver conscious about the approaching animal. This model doesn’t stop with few animals as the dataset is open-sourced the variety of animals detection keep increasing. The model gives 91% accuracy.

Keyphrases: Convolution Neural Networks, deep learning, image recognition, machine learning, road safety

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {S Sanjay and Sudhir Sidhaarthan Balamurugan and Sai Sudha Panigrahi},
  title = {Animal Detection for Road Safety Using Deep Learning},
  howpublished = {EasyChair Preprint no. 6666},

  year = {EasyChair, 2021}}
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