Quantum-AI empowered Intelligent Surveillance: Advancing Public Safety Through Innovative Contraband Detection

TL;DR

This paper introduces Quantum-RetinaNet, an innovative AI-powered surveillance model that combines quantum computing with deep learning to enhance accuracy and real-time performance in densely populated environments.

Contribution

The paper presents the novel integration of Quantum CNN with RetinaNet, creating Quantum-RetinaNet, which significantly improves speed and accuracy over traditional CNN-based surveillance systems.

Findings

Quantum-RetinaNet outperforms classical models in accuracy.

It achieves real-time processing capabilities.

The model is effective in densely populated scenarios.

Abstract

Surveillance systems have emerged as crucial elements in upholding peace and security in the modern world. Their ubiquity aids in monitoring suspicious activities effectively. However, in densely populated environments, continuous active monitoring becomes impractical, necessitating the development of intelligent surveillance systems. AI integration in the surveillance domain was a big revolution, however, speed issues have prevented its widespread implementation in the field. It has been observed that quantum artificial intelligence has led to a great breakthrough. Quantum artificial intelligence-based surveillance systems have shown to be more accurate as well as capable of performing well in real-time scenarios, which had never been seen before. In this research, a RentinaNet model is integrated with Quantum CNN and termed as Quantum-RetinaNet. By harnessing the Quantum capabilities of QCNN, Quantum-RetinaNet strikes a balance between accuracy and speed. This innovative integration positions it as a game-changer, addressing the challenges of active monitoring in densely populated scenarios. As demand for efficient surveillance solutions continues to grow, Quantum-RetinaNet offers a compelling alternative to existing CNN models, upholding accuracy standards without sacrificing real-time performance. The unique attributes of Quantum-RetinaNet have far-reaching implications for the future of intelligent surveillance. With its enhanced processing speed, it is poised to revolutionize the field, catering to the pressing need for rapid yet precise monitoring. As Quantum-RetinaNet becomes the new standard, it ensures public safety and security while pushing the boundaries of AI in surveillance.