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@article{Nasreen Jawaid_Ayaz Ali Sandeelo_Syed Hassan Ali_Syeda Nazia Ashraf_Irfan M. Leghari_Abdul Rahim_2025, title={Improving Handgun Detection: A Review and Proposal for Knowledge Graph Integration: https://doi.org/10.55966/assaj.2025.4.1.0119}, volume={4}, url={https://www.assajournal.com/index.php/36/article/view/716}, abstractNote={<p><em>Gun violence is a significant social issue, and in order to successfully identify firearms, advanced surveillance systems must be developed. Even with the introduction of deep learning algorithms like YOLO [5] and Faster R-CNN [61], it is still challenging to de- tect hidden weapons due to dynamic backdrops, shifting illumination, and partial object visibility. Even while current methods achieve high accuracy under ideal settings, they suffer significantly in real-world scenarios such object occlusion. Current handgun de- tection techniques are examined in this paper, which also divides them into deep learning and traditional approaches and identifies their drawbacks. We suggest combining knowl- edge graphs to tackle occlusion issues and false negatives by employing contextual and semantic links. Experimental validation demonstrates substantial improvements in preci- sion (94.1%) and F1-score (92.6%) compared to standalone deep learning models, with a 59% reduction in false negatives for occluded objects. The purpose of this study is to stim- ulate additional developments in reliable and all-encompassing firearm detection systems for public safety.</em></p> <p><strong><em>Keywords: </em></strong><em>Algorithms, CCTV, Deep Learning, Knowledge Graph, Handgun Detection, Object Detection, Review</em></p>}, number={01}, journal={`}, author={Nasreen Jawaid and Ayaz Ali Sandeelo and Syed Hassan Ali and Syeda Nazia Ashraf and Irfan M. Leghari and Abdul Rahim}, year={2025}, month={Aug.}, pages={2290–2303} }