Implementation of the YOLOv8 Method for Restaurant Table Condition and Customer Activity Detection
Penerapan Metode YOLOv8 untuk Deteksi Kondisi Meja dan Aktivitas Pengunjung pada Rumah Makan
DOI:
https://doi.org/10.21070/ups.12347Keywords:
Computer Vision, Object Detection, Real Time, Restaurant, YOLOv8Abstract
The culinary industry continues to grow, increasing the need for fast and efficient service. Manual table-condition monitoring in restaurants can delay customer service. This study implements and compares YOLOv8n, YOLOv8s, and YOLOv8m for real-time detection of table occupancy and conditions. The dataset consists of 500 images captured using a webcam at Soto Madura Pojok Terminal Restaurant and annotated into three classes: Empty Table, Occupied Table, and Dirty Table. The dataset was divided into 300 training images, 96 validation images, and 104 testing images based on recording sessions. The models were trained using 640 × 640 pixel images, a batch size of 16, and a maximum of 150 epochs with early stopping. Evaluation used Precision, Recall, mAP@50, and mAP@50–95. YOLOv8m achieved the best performance, with 95.82% Precision, 94.95% Recall, 97.95% mAP@50, and 94.22% mAP@50–95. The model was implemented in a dashboard to display table conditions and restaurant occupancy status.
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