This research demonstrates enhanced threat detection and automated alerts in security settings, indicating improved operational efficiency.
The demand for sophisticated security solutions has surged due to evolving security threats. This research introduces an advanced intelligent surveillance system that leverages deep learning and computer vision to automate threat detection and provide immediate alerts. Utilizing the YOLO (You Only Look Once) architecture for real-time object detection, combined with OpenCV for video processing and TensorFlow for model optimization, the system offers automated threat classification, dual-alert mechanisms (local sound alarms and email notifications), and customizable detection settings. A web-based interface developed using Flask facilitates user interaction, while MySQL manages detection logs, user preferences, and system analytics. The system demonstrates significant improvements in response time, detection accuracy, and operational efficiency over traditional CCTV systems, making it ideal for residential, commercial, and industrial security applications.
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Kumar et al. (2025) studied this question.
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