Performance Optimization and Application Research of YOLOv8 Model in Object Detection

Abstract

This paper focuses on the performance optimization and application of the YOLOv8 model in object detection. Firstly, the model architecture and optimization strategies of YOLOv8 are outlined, including network structure, feature extraction, and fusion, demonstrating its advantages in detection accuracy, inference speed, and robustness. Then, the training and inference processes of YOLOv8 are discussed in depth, addressing the issue of imbalance between positive and negative samples, and optimizing the forward propagation and non-maximum suppression algorithm. Through experimental comparisons, YOLOv8 outperforms other mainstream object detection models in multiple metrics. Finally, summarize the contributions and limitations of YOLOv8, and look forward to its future research directions and application prospects. This paper provides valuable references for the optimization and application of the YOLOv8 algorithm, aiming to provide theoretical and practical support for the further development of object detection technology in the field of computer vision.