Comparative Analysis of YOLOv8, YOLOv9, and YOLOv10 for Object Detection: Performance Metrics and Real-World Applications

摘要

Abstract: In computer vision, object detection is still an essential job with applications in robotics, autonomous cars, and surveillance. The YOLO (You Only Look Once) model family is a well-liked option for real-time object recognition because of its reputation for striking a balance between speed and accuracy. Using the Pascal VOC 2012 dataset, this study presents a comparative examination of three YOLO variants: YOLOv8, YOLOv9, and YOLOv10. Key metrics included in the research are F1-score, accuracy, recall, and mean Average accuracy (mAP). Furthermore, a loss curve analysis is performed to evaluate each model's training effectiveness. The findings show that YOLOv9, which excels in identifying smaller and more complex items, has the best recall to accuracy ratio. Although YOLOv10 exhibits a modest underperformance in peak accuracy, its improved computing efficiency renders it the best option for real-time workloads. Even though YOLOv8 is the quickest, it has trouble with little objects and complex sceneries. This study helps each model's use in a variety of real-world circumstances by offering insightful information about its advantages and disadvantages