Deep Learning-based Weapon Detection using Yolov8

Alysha Farhan,Muhammad Aftab Shafi,Marwa Gul1,Sara Fayyaz,Kifayat Ullah Bangash,Bilal Ur Rehman*, Humayun Shahid,Muhammad Kashif ()
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Alysha Farhan,Muhammad Aftab Shafi,Marwa Gul1,Sara Fayyaz,Kifayat Ullah Bangash,Bilal Ur Rehman*, Humayun Shahid,Muhammad Kashif: Department of Electrical Engineering,Faculty of Electrical and Computer Engineering, University of Engineering & Technology,Peshawar,Pakistan.Department of Telecommunication Engineering,University of Engineering & Technology, Taxila, Pakistan.

International Journal of Innovations in Science & Technology, 2025, vol. 7, issue 2, 1269-1280

Abstract: Deep learning (DL), a subset of machine learning (ML), has demonstrated remarkable success in image recognition and object detection tasks. This study presents a deep learning-based approach for offline weapon detection using the YOLOv8m architecture. A custom YOLO-formatted dataset was developed, comprising over 10,000 annotated images spanning two weapon categories: guns (all types of firearms) and knives (all types). The model achieved a Mean Average Precision (mAP@0.5) of 0.852. and mAP@0.5:0.95 of 0.622, with precision and recall scores of 0.89 and 0.80, respectively. The class-wise evaluation revealed strong detection across both weapons, with mAP@0.5 of 0.871 for knives and 0.831 for guns. Despite occasional false positives and class confusion, the system shows promise for offline weapon detection tasks.

Keywords: Weapon detection; Deep Learning; Yolov8; Object Detection; Computer Vision (search for similar items in EconPapers)
Date: 2025
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