International Journal For Multidisciplinary Research

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Weapon Detection Using Deep Learning: A Systematic Review

Author(s) Mr. Harsh Chourasia, Prof. Devendra Singh Rathore
Country India
Abstract Weapon detection is a time-critical vision task spanning public-space CCTV, body-worn cameras, and UAVs. This work presents a systematic review of deep learning approaches across visible-spectrum modalities, covering model families, datasets, evaluation protocols, and deployment constraints. We find that YOLO-family one-stage detectors dominate visible-spectrum applications by offering the best accuracy–latency trade-off for real-time inference, while X-ray research leverages large public benchmarks (e.g., SIXRay, OPIXray, PIDray) and increasingly adopts attention and anchor-free designs to handle clutter, overlap, and deformation. Performance is highly sensitive to scale-aware preprocessing, dataset realism/diversity, and, for video, temporal smoothing/aggregation. Persistent gaps include cross-site generalization, small/occluded weapon detection, rigorous calibration/uncertainty, and responsible deployment (privacy, bias, adversarial robustness). We distill best practices—compact YOLO variants with multi-scale heads and optional tiling; brightness/contrast normalization; synthetic+real data mixing; pose/orientation cues when architecture-compatible—and outline opportunities in foundation-model features, multimodal fusion (RGB–X-ray/IR), self-/active learning for long-tail classes, and standardized benchmarking with consistent mAP/F1/FPS reporting and small-object stratification..
Keywords Weapon detection, object detection, CCTV, Deep Learning, YOLO, Real-time vision
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 8, Issue 1, January-February 2026
Published On 2026-02-22
DOI https://doi.org/10.36948/ijfmr.2026.v08i01.68391
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E-ISSN 2582-2160
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