Recognizing people in images and videos is the main objective of computer vision-based person identification. The past ten years have seen a great deal of research in human detection. As single-stage algorithms, YOLO is a desirable choice for object detection because it offers faster results than two-stage algorithms. The advantage of this approach is that it provides both a manual for choosing the most effective human detection methods for real-world applications and a thorough analysis of current methods. This research paper’s objective is specifically to evaluate and compare the performance of YOLOv3, YOLOv4, and YOLOv5 models on various images to detect human in visual scenes. Additionally, this paper discusses various parameters according to which the model’s efficiency is determined. Our experimental results demonstrate that YOLOv5 achieves higher accuracy, precision, and recall as compared to other YOLO models; the highest accuracy attained by YOLOv5 is 0.94 with F1 score of 0.96.