Template-Type: ReDIF-Article 1.0
Author-Name:Alysha Farhan, Muhammad Aftab Shafi, Marwa Gul, Sara Fayyaz, Kifayat Ullah Bangash, Bilal Ur Rehman, Humayun Shahid, Muhammad Kashif
Author-Email:bur@uetpeshawar.edu.pk
Author-Workplace-Name: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
Title:Deep Learning-based Weapon Detection using Yolov8
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:Yolov8, Weapon Detection, Object Detection, Computer Vision, Deep Learning
Journal:International Journal of Innovations in Science and Technology
Pages:1269-1280
Volume:7
Issue:2
Year:2025
Month:July
File-URL:https://journal.50sea.com/index.php/IJIST/article/view/1425/1960
File-Format: Application/pdf
File-URL:https://journal.50sea.com/index.php/IJIST/article/view/1425
File-Format: text/html
Handle: RePEc:abq:IJIST:v:7:y:2025:i:2:p:1269-1280