Exploring the Frontier of Object Detection: A Deep Dive into YOLOv8 and the COCO Dataset

The field of computer vision has recently widened to incorporate object identification, which has significant impacts in areas such as autonomous vehicles, robotics, security, and healthcare. This research review and analysis look at how well YOLOv8 (You Only Look Once), the latest version of the popular object recognition model, does on the COCO data set. The purpose of this research is to learn about the YOLOv8 architecture, its improvements over previous versions, the COCO data set's make-up and evaluation metrics, and their strengths and weaknesses. From its first version through YOLOv8, the paper discusses the YOLO architecture's core features and enhancements. To establish a benchmark, the YOLOv8 model is compared to other top-tier object detection models as Faster R-CNN, SSD, and EfficientDet. The COCO data set is investigated in detail, including an overview of its pictures, annotations, item categories, data collection, and annotation procedure, and the evaluation criteria used to test object identification algorithms on this data set. The study also discusses YOLOv8 architecture and performance limits and COCO data set biases, data distribution, and annotation quality. Discussing the difficulties of generalizing YOLOv8 for diverse object detection tasks. YOLOv8 and the COCO data set are useful in real-world applications and case studies. The study concludes by offering possible enhancements for the YOLOv8 architecture, ideas for extending the COCO data set and assessment metrics and analysing upcoming trends in object identification research and their implications for YOLOv8 and COCO. This comprehensive review analysis helps object detection researchers, practitioners, and industry professionals choose models, datasets, and evaluation methods for their applications.