Advancements in Weapon Detection: A Systematic Review Using Deep Learning in Surveillance Footage

Abstract

Weapon detection in surveillance footage is a critical aspect of modern security systems, aiming to prevent potential threats and enhance public safety. Traditional methods of weapon detection often rely on manual inspection, which is labor-intensive and prone to errors. With the advent of deep learning techniques, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), there has been a significant shift towards automated and efficient weapon detection in surveillance footage. This systematic review provides an in-depth analysis of recent advancements in weapon detection using deep learning methodologies. Through a systematic search and evaluation of existing literature, this review examines the methodologies, datasets, performance metrics, and challenges encountered in various studies. It explores the effectiveness of deep learning models in detecting weapons with high accuracy and speed, while also addressing factors such as dataset diversity, annotation quality, and real-world applicability. Additionally, the review discusses the impact of transfer learning, data augmentation techniques, and model architectures on the performance of weapon detection systems. Furthermore, it highlights the role of domain adaptation and fine-tuning strategies in improving the generalization capabilities of deep learning models across different surveillance environments. The review also delves into the ethical considerations and privacy implications associated with the deployment of automated weapon detection systems in public spaces. By synthesizing findings from diverse studies, this comprehensive overview aims to provide valuable insights for researchers, practitioners, and policymakers involved in the development and implementation of advanced surveillance technologies for public safety and security purposes