(Objective)

This research aims to develop an intelligent surveillance model, Quantum-RetinaNet, by integrating a RetinaNet model with Quantum Convolutional Neural Networks (QCNN) to enhance accuracy and processing speed, thus addressing limitations of conventional CNN-based approaches. The study evaluates Quantum-RetinaNet’s performance in real-time scenarios to determine its potential as a practical and scalable solution for intelligent monitoring in densely populated areas.

(Methodology)

This research integrates a RetinaNet model with Quantum Convolutional Neural Networks (Quantum CNN or QCNN), designating the resulting framework as Quantum-RetinaNet. By harnessing the quantum capabilities of QCNN, Quantum-RetinaNet achieves a balance between accuracy and processing 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 grows, Quantum-RetinaNet offers a compelling alternative to existing CNN models, upholding accuracy standards without sacrificing real-time performance.

(Results)

The unique attributes of Quantum-RetinaNet have far-reaching implications for the future of intelligent surveillance. Its enhanced processing speed is poised to revolutionize the field, addressing the critical demand for systems that provide both rapid and precise monitoring

(Conclusions)

As Quantum-RetinaNet becomes the new standard, it ensures public safety and security while pushing the boundaries of AI in surveillance.

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