Integrating YOLOv8 and IoT in a Computer Vision System for Child Detection in Smart Cities

In an era marked by technological advancements aimed at establishing smart cities, technology increasingly focuses on enhancing aspects related to crowd management. The widespread deployment of CCTV systems, combined with the integration of computer vision, has enabled accurate insights into crowd density estimation. Our research highlights the potential benefits of child detection across various domains that serve governments and business decision-making. Leveraging Internet of Things (IoT) devices to collect real-time data and employing artificial intelligence (AI) based on deep learning through computer vision is powerful in such domains. In this paper, we propose an IoT architecture that facilitates intelligence and decision-making in two phases: 1) a deep learning model with object detection and image segmentation capabilities using YOLOv8, and 2) a tracking/counting algorithm for estimating child density based on DeepSORT. Our implementation efficiently identified and classified children in extracted images with an accuracy rate of up to 98%. Also, our model outperformed the other two solutions proposed by previous studies in terms of mAP@50, Precision, and Recall metrics. The results provide valuable insights for businesses aiming to refine site selection and guide governments in improving urban planning and safety, thereby fostering sustainable and intelligent urban development.

@article{Alotaibi2025,
  title     = {Integrating YOLOv8 and IoT in a Computer Vision System for Child Detection in Smart Cities},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {9},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Modhawi Alotaibi and Atheer Alruwaythi and Sara Alenazi and Maisaa Alsaedi},
  doi       = {10.14569/IJACSA.2025.0160960},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160960}
}