Intelligent ammunition detection and classification system using convolutional neural network

Gulzar Ahmad, Saad Alanazi, Madallah Alruwaili, Fahad Ahmad, Muhammad Adnan Khan, Sagheer Abbas, Nadia Tabassum

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Security is a significant issue for everyone due to new and creative ways to commit cybercrime. The Closed-Circuit Television (CCTV) systems are being installed in offices, houses, shopping malls, and on streets to protect lives. Operators monitor CCTV; however, it is difficult for a single person to monitor the actions of multiple people at one time. Consequently, there is a dire need for an automated monitoring system that detects a person with ammunition or any other harmful material Based on our research and findings of this study, we have designed a new Intelligent Ammunition Detection and Classification (IADC) system using Convolutional Neural Network (CNN). The proposed system is designed to identify persons carrying weapons and ammunition using CCTV cameras. When weapons are identified, the cameras sound an alarm. In the proposed IADC system, CNN was used to detect firearms and ammunition. The CNN model which is a Deep Learning technique consists of neural networks, most commonly applied to analyzing visual imagery has gained popularity for unstructured (images, videos) data classification. Additionally, this system generates an early warning through detection of ammunition before conditions become critical. Hence the faster and earlier the prediction, the lower the response time, loses and potential victims. The proposed IADC system provides better results than earlier published models like VGGNet, OverFeat-1, OverFeat-2, and OverFeat-3.
Original languageEnglish
Pages (from-to)2585-2600
Number of pages16
JournalComputers, Materials and Continua
Issue number2
Publication statusPublished - 5 Feb 2021


  • CCTV
  • CNN
  • IADC
  • deep learning
  • intelligent ammunition detection
  • DnCNN

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