IMPLEMENTASI SMART GATE BERBASIS YOLOv8n UNTUK DETEKSI KELENGKAPAN TOPI DAN DASI YOLOv8n-BASED SMART GATE IMPLEMENTATION FOR HAT AND TIE COMPLETENESS DETECTION
Keywords:
ESP32-S3;, YOLOv8n;, object detection;, smart gate;, school uniformAbstract
This study implements a local-network smart gate prototype for monitoring hat and tie completeness using an ESP32-S3 Camera and YOLOv8n. The camera acquires JPEG frames and transmits them through Wi-Fi to a Python Flask backend, where OpenCV and YOLOv8n perform object detection; a React dashboard presents detection status and stores operational logs. The dataset contains 3,130 images divided into 2,471 training, 326 validation, and 333 testing images. The active model was trained for 10 epochs at 320 × 320 pixels on CPU and was executed by the backend at 640-pixel inference size with a confidence threshold of 0.20. The best validation epoch produced 90.824% precision, 87.958% recall, 94.314% mAP50, and 63.870% mAP50–95. Eight dashboard observations showed 7.04–11.62 FPS with an average of 8.79 FPS. Functional tests confirmed camera streaming, hat and tie detection, status display, image storage, and CSV logging. The current completeness decision is still frame-based, so further development should associate attributes with each individual and integrate the physical gate trigger.
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