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Weapon Detection System on Android Devices: A Portable Machine Learning Technology

Published: 07 May 2024 Publication History

Abstract

The machine learning techniques paired with augmented reality (AR) systems extend the capabilities of surveillance systems for the social good through analyzing and extracting complex patterns in a simple and cost-effective AR-based app running on any mobile device. The potential of this app in crime prevention provides opportunities for this study to investigate the development of a portable, inexpensive surveillance system using an Android device that mainly focuses on detecting deadly weapons to promote public safety, especially in urbanized areas. The detection of deadly weapons beforehand is a proactive act before the crime happens. The study utilized the detection models offered by TensorFlow, an open-source machine-learning platform with a broad and adaptable ecosystem of tools and libraries. After the functional and performance tests, the study found that the AR-based weapon detection system could detect the weapons and recognize and classify them based on the computation of the detection confidence. Thus, the cost-efficient and portable system showcased its potential as a proactive surveillance tool that detects and alerts for the weapon's existence. However, there is still a need to improve its detection capability for it to be able to increase its detection rate, especially in crowded areas, at far distances, in dark places, and those obstructed and concealed weapons.

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  1. Weapon Detection System on Android Devices: A Portable Machine Learning Technology

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    APIT '24: Proceedings of the 2024 6th Asia Pacific Information Technology Conference
    January 2024
    105 pages
    ISBN:9798400716218
    DOI:10.1145/3651623
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 07 May 2024

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    Author Tags

    1. Android App
    2. Artificial and Computational Intelligence
    3. Augmented Reality
    4. Machine Learning
    5. Mobile and Ubiquitous Solutions
    6. Social Good
    7. TensorFlow Utilization
    8. Weapon Detection

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