SmartGuard: A Deep Learning and IoT-Enabled Smart Surveillance Framework for Data-Driven Community Safety and Threat Prevention

Abstract

Conventional closed-circuit television (CCTV) systems record crime rather than prevent it. This paper presents SmartGuard, a smart surveillance framework that combines a fine-tuned YOLOv8-nano deep learning model with an IoT alert pipeline to detect masked or disguised individuals in real time and notify residents before harm occurs. Running on a Raspberry Pi 4 edge device, the system achieves a masked-individual detection mAP50 of 0.995 with sub-1.3 second end-to-end alert latency via Firebase Cloud Messaging. A pilot survey of 32 University of East London participants returned an overall approval mean of 3.88/5. The system avoids facial recognition entirely, operating within UK GDPR constraints. Results demonstrate that effective, low-cost, privacy-conscious residential surveillance is technically feasible on affordable edge hardware.