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The necessity for intelligent monitoring has grown more urgent as the number of crimes involving firearms and knives in homes and public areas has increased. Traditional CCTV systems require human operators, whose attentiveness and the impracticability of monitoring multiple video feeds simultaneously limit their effectiveness. Artificial intelligence (AI)-based vision systems can automatically detect firearms and enhance public safety, thereby overcoming this constraint. In accordance with the Preferred Reporting Items for Systematic Reviews (PRISMA) criteria, a systematic evaluation of AI-based weapon detection for security monitoring is conducted. The paper summarizes research works on AI, machine learning, and deep learning techniques for identifying weapons in surveillance footage from 2016 to 2025, encompassing 101 research papers. The reported precision ranged from 78% to 99.5%, recall ranged from 83% to 97%, and mean average precision (mAP) ranged from approximately 70% to 99%. While AI-based monitoring significantly enhances detection accuracy, issues with inconsistent evaluation criteria, limited real-world validation, and dataset variability persist. The research study emphasizes the need for uniform benchmarking, robust privacy protections, and standardized datasets to ensure the ethical and reliable implementation of AI-driven weapon-detection systems.
Thangavel et al. (Mon,) studied this question.