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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Robotics and Automation (IJRA) International Journal of Reconfigurable and Embedded Systems (IJRES) International Journal of Informatics and Communication Technology (IJ-ICT) International Journal of Advances in Applied Sciences Bulletin of Electrical Engineering and Informatics
Uddin, Jia
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Automotive Engineering Computer Science & IT Earth & Planetary Sciences Economics, Econometrics & Finance Electrical & Electronics Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Journal : Bulletin of Electrical Engineering and Informatics

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Real-time IoT security framework for detecting a person with a weapon using Raspberry Pi, Google Vertex AI, and AWS Schutte, Storm; Uddin, Jia
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.7627

Abstract

Realtime crime scene detection is a vital issue for ensuring security in various environments. Building on recent advancements in machine learning algorithms, this paper presents an IoT framework for real-time weapon and face detection. By deploying a convolutional neural network (CNN) architecture in Vertex AI and utilizing the portable camera module of a Raspberry Pi, to detect whether a person is carrying a weapon. This is achieved by pre-processing, which we resize and annotate the images. Then, train and validate the CNN model with the annotated label dataset. The trained model is saved in Google Cloud’s Vertex AI portal. Then we tested the model by uploading live images from a camera as well as a few video clips, to a Django application in amazon web hosting services (AWS) to Vertex AI. The model exhibited an accuracy of 97.2% along with a F1 score of 0.97. In addition, the model outperforms the other state-of-the-art models by less trainable parameters and higher accuracy.
Co-Authors Ali, Md. Suhag Begum, Momotaz Chakrabarty, Amitabha Chowdhury, Mohammad Asaduzzaman Das, Bimal Chandra Ferdoush, Zannatul Ghosh, Kingkar Prosad Haque, Shahina Hossain, Fahad Hossain, Monir Hossen, Thofazzol Islam, Md. Monirul Islam, Towhidul Jamee, Syed muhammad Hasnat Jibon, Ferdaus Anam Juthi, Jafrin Sultana Kashem, Mohammod Abul Mahmud, Booshra Nazifa Mustary, Shabnom Rafi, Jakaria Khan Rahman, Shakila Sajib, Abdul Aziz Schutte, Storm Shuvo, Mehedi Hasan Ullah, Nayeem