YOLO Based IoT Tracking for Academic Labs on Raspberry Pi

Academic IoT laboratories often rely on shared equipment, making efficient borrow-return management essential. Conventional management methods depend on manual recording, which can cause verification delays, data entry errors, and increased staff workload. This study proposes a YOLO based IoT equipment tracking system that uses a camera to detect and count devices inside student equipment boxes for borrow-return recording and inventory monitoring. The system runs on a Raspberry Pi 5 for real-time edge-based processing, while detection results are stored in a database and displayed through a web-based dashboard. Experimental results show that the YOLO12n model achieved an F1-score of 0.996 with a real-time inference speed of 12.01 FPS, demonstrating the system's effectiveness in reducing human error and improving laboratory inventory management efficiency.