Real-Time Image Segmentation and Object Tracking for Autonomous Vehicles

Autonomous vehicles rely on advanced perception systems to navigate dynamically changing environments. This research aims to develop a real-time image segmentation and object tracking system, leveraging deep learning techniques, to enhance the perceptual capabilities of autonomous vehicles. Thanks to developments in sensing, machine learning, and artificial intelligence, autonomous cars are getting closer to reality. Vehicle detection is one of the most important parts of autonomous driving systems, and it’s critical to the effectiveness and safety of these systems. The main intention of this research is to enquire how autonomous vehicles employ the cutting- edge YOLOv8 (You Only Look Once version 8) algorithm for vehicle recognition. A real-time object detection model called YOLOv8 has demonstrated remarkable results in identifying and categorising items in pictures and videos. It is the most recent model in the YOLO series and is renowned for striking a balance between accuracy and speed of detection.