Deep Learning-Driven Surveillance for Intrusion Identification using Proactive Intelligent Video Recognition

In today’s security-conscious environment, the need for effective real-time weapon detection systems is paramount, especially in public spaces and sensitive areas. The primary objective of this research is to create a real-time system for detecting weapons utilizing advanced deep learning techniques, namely VGG16 and Faster RCNN. The system’s objective is to precisely detect and categorize weapons from photos and video data, offering prompt notifications and improving security procedures. The project’s backdrop explores the difficulties encountered, including data gathering, precision standards, immediate processing, confidentiality, and deployment considerations. The system achieves excellent accuracy and real-time detection capabilities by building a comprehensive dataset manually and training the models using GPUs and modern technologies. Using Python as the programming language provides flexibility and simplicity in development, making use of Python’s modules such as OpenCV for image processing and Keras for deep learning models. The Tkinter framework enables the creation of a graphical user interface (GUI) that supports various user operations, such as uploading datasets, generating models, processing images and videos, detecting weapons, and visualizing results. The methodology employs a systematic approach, encompassing stages such as data preprocessing, model building, training, testing, and result analysis. The combination of VGG16 and Faster RCNN algorithms demonstrates a compromise between speed and accuracy, with Faster RCNN exhibiting greater performance in real-time detection. The project aims to achieve several objectives, including the compilation of a dataset, training a model, evaluating accuracy, implementing real-time detection, and establishing alerting methods. The system’s range encompasses a wide range of surroundings, lighting situations, and orientations, allowing it to be flexible and suitable for a variety of security applications. To summarize, the real-time weapon detection system outlined in this study offers a strong foundation for improving security measures. Further improvements can be made in terms of scalability, efficiency, and wider implementation situations.