YOLO Based IoT Tracking for Academic Labs on Raspberry Pi

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dc.contributor.authorJiamrachada, Tamakorn
dc.contributor.authorNonsiri, Sarayut
dc.contributor.authorKamin, Pichitchai
dc.contributor.authorNanthajirapong, Nathaphon
dc.date.accessioned2026-08-06T10:55:53Z
dc.date.available2026-08-06T10:55:53Z
dc.date.issued2026-06-16
dc.description.abstractAcademic 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.
dc.identifier.citationIait 2026 14th International Conference on Advances in Information Technology, 2026
dc.identifier.doi10.1145/3816713.3819505
dc.identifier.other2-s2.0-105045242536
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/18200
dc.sourceIait 2026 14th International Conference on Advances in Information Technology
dc.subjectEdge Computing
dc.subjectInternet of Things (IoT)
dc.subjectLaboratory Equipment Management
dc.subjectObject Detection
dc.subjectRaspberry Pi
dc.subjectYOLO
dc.titleYOLO Based IoT Tracking for Academic Labs on Raspberry Pi
dc.typeConference Paper

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