Raspberry Pi-Based Real-Time Object Recognition for Smart Item Recovery

Year : 2025 | Volume : 12 | Issue : 03 | Page : 28 36
    By

    Amit Munjal,

  • Mohit Agarwal,

  • Anant Agarwal,

  • Divya,

  1. Research Scholar, Department of ECE Thapar Institute of Engg. & Tech. , Patiala, Punjab, India
  2. Research Scholar, Department of ECE Thapar Institute of Engg. & Tech. , Patiala, Punjab, India
  3. Research Scholar, Department of ECE Thapar Institute of Engg. & Tech. , Patiala, Punjab, India
  4. Research Scholar, Department of ECE Thapar Institute of Engg. & Tech. , Patiala, Punjab, India

Abstract

In today’s fast-paced world, individuals often lose valuable time searching for misplaced items such as keys, phones, and remote controls—an estimated 2.5 days per year. This paper introduces a cost-effective, computer vision-based system that helps users efficiently locate everyday objects. The system utilizes a 1080p camera and the YOLO (You Only Look Once) object detection algorithm to enable accurate, real-time object recognition. Designed for practical usability, it stores detected object data for future reference, enhancing repeatability and reliability. By combining affordability with high-performance visual detection, the system offers a scalable solution for various environments, including homes, offices, and assistive settings. This approach minimizes search time and enhances user convenience, making it a valuable tool for improving organization and accessibility in daily life. It is implemented on a Raspberry Pi 4, providing a compact and accessible platform for deployment. To enhance user interaction, a mobile application compatible with various devices allows users to search and locate items by name. By combining machine learning, real-time detection, and cloud-based access, this system minimizes the effort required to find lost objects and helps users reclaim time for more essential activities.

Keywords: Object Detection, Computer Vision, Real-Time Tracking, Smart Finder System

[This article belongs to Recent Trends in Sensor Research & Technology ]

How to cite this article:
Amit Munjal, Mohit Agarwal, Anant Agarwal, Divya. Raspberry Pi-Based Real-Time Object Recognition for Smart Item Recovery. Recent Trends in Sensor Research & Technology. 2025; 12(03):28-36.
How to cite this URL:
Amit Munjal, Mohit Agarwal, Anant Agarwal, Divya. Raspberry Pi-Based Real-Time Object Recognition for Smart Item Recovery. Recent Trends in Sensor Research & Technology. 2025; 12(03):28-36. Available from: https://journals.stmjournals.com/rtsrt/article=2025/view=235199


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Regular Issue Subscription Review Article
Volume 12
Issue 03
Received 05/07/2025
Accepted 02/08/2025
Published 30/12/2025
Publication Time 178 Days
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