Harnessing Deep Learning for Video Based Weapon Detection

Abstract

This research addresses the escalating global issue of handgun-related crimes by proposing an innovative Intelligent Video Surveillance System (IVSS) that leverages advanced deep learning (DL) techniques for remote firearm detection and timely threat response. The system employs Convolutional Neural Networks (CNN) and the YOLO v3 model, uniquely integrating Transfer Learning (TL) to enhance adaptability and efficacy. Experimental validation using the Internet Movie Firearms Database (IMFDB) demonstrates the system's versatility in detecting various pistols and guns, achieving promising results that surpass existing systems in accuracy and efficiency. Challenges in real-time weapon recognition, such as the absence of a standardized weapon dataset, occlusion, and small object sizes, are acknowledged. Emphasis is placed on the critical need for reliable data acquisition, precise labeling, and preprocessing tailored to different detection algorithms. The implementation encompasses video collection, preprocessing, model loading, algorithm application, segmentation, and classification, alongside a user-friendly webcam interface for real-time detection. Additionally, the system integrates the pyttsx3 library for voice alerts and the Twilio API for voice call alerts to enhance responsiveness. In summary, this study presents a novel CNN-based model combining Transfer Learning with YOLO v3, achieving superior weapon identification and distinguishing between real and fake firearms, representing a significant advancement in intelligent video surveillance and contributing to the reduction of weapon violence.