Crowd Detection: Leveraging YOLO for Human Recognition

Year 2025, Volume: 9 Issue: 3, 571 - 577, 01.07.2025

Gülsüm Yiğit

https://doi.org/10.31127/tuje.1627839

Abstract

Human detection in crowded environments is essential for applications such as surveillance, autonomous navigation, and crowd management. This study examines the performance of various YOLO (You Only Look Once) models in detecting humans. We combined four public human detection datasets to create a comprehensive dataset for crowd detection. Experiments were conducted on YOLOv5, YOLOv8, and YOLOv11 models, employing different architectures and model sizes. Performance was evaluated using mean Average Precision (mAP) at Intersection over Union (IoU) thresholds of 50% (mAP@50) and across 50-95% (mAP@50-95). The results indicate that the YOLOv8m model achieved the highest mAP@50 of 0.944 and mAP@50-95 of 0.697, surpassing larger models such as YOLOv11x, which attained 0.90 and 0.632 respectively. Additionally, other YOLOv8 variants demonstrated superior or comparable performance to their YOLOv5 and YOLOv11 counterparts. These findings highlight the effectiveness of YOLOv8’s optimized structures in delivering accurate and efficient human detection in high-density settings.

Keywords

Human Detection , Yolo Models , Crowd Detection , Object Detection , Mean Average Precision

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