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- W4380605391 abstract "With increasing terrorist and criminal activities around the world, detecting several types of weapons at the same time has become crucial. Especially most of these groups are used lightweight weapons which are easily lifted and run with them. For that reason, finding an intelligent system capable of discovering these types of weapons with high quality and speed becomes of utmost necessity. This paper focuses on utilizing the deep learning networks technique (namely YOLOV5) to detect various weapons with high accuracy and speed. YOLOV5 is a member of compound-scaled object detection, which is commonly trained on the Microsoft COCO dataset. However, the proposed system was trained on the self-collected dataset, which consists of sixty thousand images of different types of guns used in Iraq. Thus, the proposed system has the ability to detect different types of weapons at the same time. Furthermore, the system can achieve high performance by increasing the number of trained images for every kind of weapon. The results showed a high mAP reach of 97% of some types of weapons." @default.
- W4380605391 created "2023-06-15" @default.
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- W4380605391 date "2022-11-15" @default.
- W4380605391 modified "2023-09-23" @default.
- W4380605391 title "A Multi-Weapon Detection Using Synthetic Dataset and Yolov5" @default.
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- W4380605391 doi "https://doi.org/10.1109/csctit56299.2022.10145737" @default.
- W4380605391 hasPublicationYear "2022" @default.
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