Computer Vision based Animal Collision Avoidance Framework for Autonomous Vehicles
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Autonomous VehiclesCollision AvoidanceLane DetectionObject TrackingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
A neural model of the locust visual system for detection of object approaches with real-world scenes
In the central nervous systems of animals like pigeons and locusts, neurons were identified which signal objects approaching the animal on a direct collision course. Unraveling the neural circuitry for collision avoidanc…
Collision AvoidanceEnd to end collision avoidance based on optical flow and neural networks
Optical flow is believed to play an important role in the agile flight of birds and insects. Even though it is a very simple concept, it is rarely used in computer vision for collision avoidance. This work implements a n…
Collision AvoidanceOptical Flow EstimationFIR-based Future Trajectory Prediction in Nighttime Autonomous Driving
The performance of the current collision avoidance systems in Autonomous Vehicles (AV) and Advanced Driver Assistance Systems (ADAS) can be drastically affected by low light and adverse weather conditions. Collisions wit…
Autonomous DrivingAutonomous VehiclesCollision AvoidanceDecoder+2Autonomous Vision-based UAV Landing with Collision Avoidance using Deep Learning
There is a risk of collision when multiple UAVs land simultaneously without communication on the same platform. This work accomplishes vision-based autonomous landing and uses a deep-learning-based method to realize coll…
Collision AvoidanceDeep LearningMachine Learning Subsystem for Autonomous Collision Avoidance on a small UAS with Embedded GPU
Interest in unmanned aerial system (UAS) powered solutions for 6G communication networks has grown immensely with the widespread availability of machine learning based autonomy modules and embedded graphical processing u…
Collision AvoidanceDepth EstimationGPU