Authors: Mr.S. Sathish, Ms. A. Sangeetha
Abstract: Object detection is a key area in computer vision with wide-ranging applications such as autonomous driving, surveillance, and augmented reality. YOLOv8, an advanced version of the YOLO series, stands out for its high accuracy and real-time performance. This study focuses on the analysis and implementation of YOLOv8 for real-time object detection, emphasizing its architecture that employs a deep neural network to perform a single forward pass for predicting bounding boxes and class probabilities simultaneously. The model’s main components—backbone network, detection layers, and anchor boxes—work together to achieve fast and efficient detection. Practical aspects such as model optimization, GPU acceleration, and post-processing are also explored to enhance speed and accuracy. Experiments conducted on benchmark datasets and real-world data demonstrate YOLOv8’s effectiveness, proving it to be a robust and adaptable solution for real-time object detection tasks. This research contributes to the advancement of computer vision and provides practical insights for deploying YOLOv8-based detection systems across multiple domains.
