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All Journal IJISCS (International Journal of Information System and Computer Science)
Donatien Kadima Muamba
Faculté des Sciences Informatiques, Université Révérend KIM
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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REAL-TIME DETECTION OF PROHIBITED OBJECTS IN PUBLIC SPACES BASED ON IOT AND DEEP LEARNING Donatien Kadima Muamba; David Muanza Lubukayi; Ali Akake Nengo
IJISCS (International Journal of Information System and Computer Science) Vol 10, No 1 (2026): IJISCS (International Journal of Information System and Computer Science)
Publisher : Bakti Nusantara Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/ijiscs.v10i1.1878

Abstract

Security in public spaces is a major challenge in the face of diversifying threats. Traditional video surveillance systems, relying on continuous human supervision, have limitations in terms of responsiveness and reliability. This article proposes an intelligent system for detecting prohibited objects, combining the Internet of Things (IoT) and deep learning. The architecture is based on an embedded ESP32-CAM module for image acquisition and a backend server using a deep learning model for analysis. Experimental results show an overall accuracy of 92.8%, demonstrating the suitability of this approach for real-time automated surveillance applications
Co-Authors Ali Akake Nengo David Muanza Lubukayi