Computer Vision based Animal Collision Avoidance Framework for Autonomous Vehicles

TL;DR

This paper presents a computer vision framework utilizing deep learning for detecting animals on highways to prevent vehicle collisions, enhancing autonomous vehicle safety in animal-populated regions.

Contribution

The paper introduces a novel deep learning-based system combining Mask R-CNN and lane detection for real-time animal collision avoidance in autonomous vehicles.

Findings

Effective animal detection across various conditions

Accurate lane and movement tracking of animals

Potential to reduce vehicle-animal collision incidents

Abstract

Animals have been a common sighting on roads in India which leads to several accidents between them and vehicles every year. This makes it vital to develop a support system for driverless vehicles that assists in preventing these forms of accidents. In this paper, we propose a neoteric framework for avoiding vehicle-to-animal collisions by developing an efficient approach for the detection of animals on highways using deep learning and computer vision techniques on dashcam video. Our approach leverages the Mask R-CNN model for detecting and identifying various commonly found animals. Then, we perform lane detection to deduce whether a detected animal is on the vehicle's lane or not and track its location and direction of movement using a centroid based object tracking algorithm. This approach ensures that the framework is effective at determining whether an animal is obstructing the path or not of an autonomous vehicle in addition to predicting its movement and giving feedback accordingly. This system was tested under various lighting and weather conditions and was observed to perform relatively well, which leads the way for prominent driverless vehicle's support systems for avoiding vehicular collisions with animals on Indian roads in real-time.