ANIMAL DETECTION FOR ROAD SAFETY USING DEEP LEARNING

Abstract

ANIMAL DETECTION FOR ROAD SAFETY USING DEEP LEARNING project aims to develop a system that can detect animals on roads to improve road safety for both drivers and animals. Animal-vehicle collisions are a major cause of road accidents worldwide and can lead to injuries, fatalities, and significant economic losses. The project proposed a deep learning-based approach for animal detection in real-time. The system used a combination of image processing techniques and machine learning algorithms to accurately detect and classify animals in different weather conditions and lighting conditions. It also takes into account the behaviour of different animal species and adjust its detection algorithms accordingly. The outcome of this project is a deep learning-based animal detection system that can be integrated into existing road safety systems to improve the safety of drivers and animals. The system has the potential to significantly reduce the number of animal-related accidents on our roads and protect both drivers and animals