An analysis of YOLO models versus RT-DETR applied to multi-object detection in images

摘要

Object detection is one of the critical and essential tasks in computer vision, with applications ranging from surveillance and industrial control to robotics and image analysis. This research presents a performance analysis of different YOLO (You Only Look Once) versions and a transformer model. The study evaluates YOLOv8, YOLOv9, YOLOv10, YOLOv11, and the RT-DETR (Real-Time Detection Transformer) model for object detection. The experiment uses a dataset of 1,730 images classified into five types: birds, dogs, cats, plants, and fruits, each with its subtypes. Also, we test with a frog dataset of 613 images which are characterized as complex images because present occlusion, complex backgrounds and variations in illumination. In addition, its performance is evaluated using standard metrics such as Precision, Recall, mAP50, and mAP50-95.