Performance Comparison of YOLOv10, YOLOv11, and YOLOv12 Models on Human Detection Datasets

Abstract

One popular of object detection model for object detection is You Only Look Once (YOLO) with humans are among the most often utilized for detection objects. Despite the various of human datasets, just a few research that compared the datasets performance against various versions of the YOLO algorithm. This study compares the performance of YOLOv10, YOLOv11, and YOLOv12 on eight different datasets, such as CrowdHuman, CityPersons, Wider Person, Mall Dataset, INRIA, Microsoft Common Object (MS COCO), PASCAL VOC, and MOT17. Precision, recall, mAP@50, and mAP@50-95 are used to measure the YOLO model version's performance on each dataset. The results indicate that each datasets have different perfomance on each version of YOLO, so the performance on model depends on the variation of the dataset. The best results on the MOT17 dataset are obtained by YOLOv12, with 0.909 in precision, 0.775 in recall, 0.88 in mAP@50, and 0.695 in mAP@50-95. On the City Person dataset. However, YOLOv11 performs best result, with 0.782 in precision, 0.529 in recall, 0.694 in mAP@50, and 0.476 in mAP@50-95. Therefore, choosing a YOLO version that is appropriate for the dataset's complexity is essential to creating the best detection model Therefore, selecting the appropriate YOLO version according to the dataset complexity is crucial to obtain the most optimal detection model.