Symposium on Applied STEM, Maritime Technology, and Sustainable Engineering

Informatics, Data Systems, and Digital Innovation

PPAVI-2026-2C1BBB79 Poster IoT monitoring

Computer Vision-Based Smart Inventory and Real-Time Power Monitoring Using Edge Computing

Authors: Daniswara Satya Pradipta, Muhammad Habib

Supervisor: Wahyu Setiady, S.T., M.T.

Affiliation: Politeknik Industri ATMI

Poster

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Abstract

Component inventory management in engineering laboratories and workshops can be hindered by inefficient manual record-keeping, human error, and the risk of misplacing or misidentifying items. This study presents a smart inventory and electrical power monitoring system implemented on a Raspberry Pi 3B+ with 1 GB of RAM. The image-processing pipeline combines YOLOv5n object detection, executed through the OpenCV DNN C++ engine, with an ensemble TensorFlow Lite classifier and HSV-based color feature extraction to recognize components such as Arduino UNO boards, ESP32 WROOM-32 modules, 16 × 2 LCDs, breadboards, and smartphones. A local Flask-based dashboard supports item storage, retrieval, and borrowing records through a pushbutton-triggered vision mechanism with a 2.5-second hysteresis interval. Electrical monitoring uses a PZCT-02 current sensor and an ADS1115 16-bit I²C analog-to-digital converter to measure AC current, active power, and cumulative energy consumption. The system stores data locally in an SQLite database configured with Write-Ahead Logging (WAL). Initial performance testing showed an operating rate of 15–30 frames per second and RAM usage below 100 MB. These results indicate that the prototype can perform inventory recognition and power monitoring locally without relying on cloud services. Further testing is needed to evaluate recognition accuracy, inventory-record reliability, and measurement accuracy under varied operating conditions.

Keywords

edge computing, smart inventory, computer vision, YOLOv5n, TensorFlow Lite, Raspberry Pi, electrical power monitoring

Conference Certificate

Open Certificate

Certificate No. PPAVI-2026-2C1BBB79

Certificate

of Presentation

This certificate is formally awarded to:

Daniswara Satya Pradipta, Muhammad Habib, Wahyu Setiady

for presenting the paper entitled:

Computer Vision-Based Smart Inventory and Real-Time Power Monitoring Using Edge Computing

PPAVI-2026-2C1BBB79 / Poster

The 1st PPAVI Student Research and Vocational Conference 2026

16 Oct 2026 - 31 Oct 2026

Informatics, Data Systems, and Digital Innovation

IoT monitoring

RD. Dr. Antonius Ary Setyawan, S.S., M.A, M.M.

Ketua Pengurus PPAVI

Romanus Edy Prabowo, S.Si., M.Sc., Ph.D.

Ketua Panitia

Digitally issued by PPAVI Conference System on 16 October 2026. Available at:

https://ppavi.or.id/conferences/ppavi-student-research-vocational-conference-2026/presentations/PPAVI-2026-2C1BBB79/