Fine-Tuning YOLOv8s for Unified Human and Face Detection in Crowded Environments

Oussama Lachihab; Ahmed El Kiram; Latifa Errajy

doi:10.48084/etasr.15307

Authors

Oussama Lachihab Department of Computer Science, Laboratory of Computer Science and Smart Systems, Faculty of Sciences Semlalia, Cadi Ayyad University, Marrakech, Morocco
Ahmed El Kiram Department of Computer Science, Laboratory of Computer Science and Smart Systems, Faculty of Sciences Semlalia, Cadi Ayyad University, Marrakech, Morocco
Latifa Errajy Department of Computer Science, Laboratory of Computer Science and Smart Systems, Faculty of Sciences Semlalia, Cadi Ayyad University, Marrakech, Morocco

Volume: 16 | Issue: 1 | Pages: 32348-32356 | February 2026 | https://doi.org/10.48084/etasr.15307

Received: 4 October 2025 | Revised: 2 November 2025 | Accepted: 12 November 2025 | Online: 9 February 2026

Corresponding author: Oussama Lachihab

Abstract

Accurate detection of human bodies and faces in densely populated scenes remains challenging due to occlusions and overlapping instances. This paper presents a lightweight object detection solution built upon the You Only Look Once version 8 small (YOLOv8s) architecture, fine-tuned for challenging urban scenes where occlusion, density, and limited computing resources are common. Leveraging an enhanced dataset with detailed person and face annotations, our model achieves a good mean Average Precision at IoU threshold 0.5 (mAP@0.5) of 57.61%, with particularly robust performance on full-body human detection (Average Precision (AP) = 73.5%). Despite moderate face detection accuracy (AP = 42.1%), qualitative results demonstrate solid performance under real-world constraints. The model's compact size and high inference speed make it ideally suited for deployment on edge devices, such as mobile cameras and embedded Artificial Intelligence (AI) systems. A compelling use case is explored through the lens of crowd monitoring in Jamaa El-Fna square in Marrakech, a bustling and high-density public space that demands real-time situational awareness. This work offers a practical tool for urban analytics and public safety, and it lays the foundation for future improvements in face detection, post-processing, and real-time system integration.

Keywords:

YOLOv8s, object detection, human detection, face detection, edge AI, real-time inference, crowd analysis, urban monitoring

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