Humanoid Object Detection Appplication Moving in Open Space using YOLOv8
Aplikasi Deteksi Objek Humanoid Bergerak pada Ruang terbuka Menggunakan YOLOv8
DOI:
https://doi.org/10.21070/ups.8481Keywords:
Deep Learning, Humanoid Object Detection, Open Space, YOLOv8Abstract
This study applies the YOLOv8 algorithm to detect humanoid objects in open environme 0nts, specifically in school areas such as parking lots. The main objective is to develop an intelligent system capable of identifying students based on four types of uniforms: none, grey, batik, and department-specific. Data were collected from CCTV footage and processed using Roboflow, resulting in 314 images with 1,649 bounding boxes. The dataset was divided into training and validation sets, with a .yaml configuration used to train the YOLOv8s model. Training was conducted with variations in image size, batch size, and epochs to optimize performance. Evaluation results showed a precision of 0.86, recall of 0.92, and mAP\@0.50 of 0.93. Visual testing indicated an overall detection accuracy of 85%, although minor errors occurred in distinguishing between batik and department uniforms. This study demonstrates the reliability of YOLOv8, with future research aiming to expand the dataset and object categories.
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