Performance Evaluation of YOLOv8 for Object Detection on the COCO Dataset

Abstract

In computer vision, object detection is a crucial task with many applications, such as robots, autonomous cars, and surveillance. The newest model in the YOLO (You Only Look Once) family, YOLOv8, brings important architectural enhancements to improve model efficiency, speed, and detection accuracy. YOLOv8 is a small object detection framework based on deep learning that has been used a lot in computer vision tools. Building blocks, preparation, and data addition methods are used to make it work. The training process was carefully set up to work with the limited resources that were available while still using modern deep learning techniques. The model was then refined on the custom dataset that was made just for the job. In terms of precision, recall, and class-wise detection ability, the results show some good signs. Our findings show that YOLOv8 is appropriate for both cloud-based and embedded applications since it maintains real-time inference speeds while achieving excellent detection accuracy. The results demonstrate YOLOv8's potential as an effective tool for a variety of real-world object detection applications.