IoT Sensor-Based Convolutional Neural Network System for Concealed Weapon Detector for Security Enhancement
Moradeke Grace Adewumi1, Ese Sophia Mughele2, Akinribido Comfort Tomiye3, Rowland Ogunrinde4, Stella Chinye Chiemeke5, Olumide Sunday Adewale6
1Moradeke Grace Adewumi, Department of Computing and Information Science, Bamidele Olumilua University of Education, Science and Technology Ikere-Ekiti. Ekiti State Nigeria.
2Ese Sophia Mughele, Department of Cyber Security, University of Delta, Agbor. Delta State, Nigeria.
3Akinribido Comfort Tomiye, Department of Computing and Information Science, Bamidele Olumilua University of Education, Science and Technology Ikere-Ekiti. Ekiti State Nigeria.
4Rowland Ogunrinde, Department of Computing and Information Science, Bamidele Olumilua University of Education, Science and Technology Ikere-Ekiti. Ekiti State Nigeria.
5Stella Chinye Chiemeke, Department of Cyber Security, University of Delta, Agbor. Delta State, Nigeria.
6Olumide Sunday Adewale, Department of Computer Science, Federal University of Technology, Akure. Ondo State, Nigeria.
Manuscript received on 12 January 2024 | Revised Manuscript received on 10 November 2024 | Manuscript Accepted on 15 November 2024 | Manuscript published on 30 November 2024 | PP: 19-25 | Volume-4 Issue-2, November 2024 | Retrieval Number: 100.1/ijcns.B143004021124 | DOI: 10.54105/ijcns.B1430.04021124
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© The Authors. Published by Lattice Science Publication (LSP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Security has been a major concern in our societies due to the rise in crime rate, most especially in a crowded area. Concealed weapons have been posing a significant threat to government, law enforcement, security agencies, and civilians. Existing weapons detection systems seem to be not culpable of detecting concealed weapons without the cooperation of the person being searched. There remains a need for a weapons detector that can detect and identify concealed weapons for security enhancement in Nigeria. For this purpose, computer vision methods and a deep learning approach were applied for the identification of a weapon from captured images downloaded from the internet as a prototype for the study. Recent work in deep learning and machine learning using convolutional neural networks has shown considerable progress in the areas of object detection and recognition. The CNN algorithms are trained on the collected datasets to identify and differentiate between weapons and non-weapons. We built a concealed weapon detection system prototype and conducted a series of experiments to test the system’s accuracy, precision, and false positives. The models were compared by evaluating their average values of sensitivity, specificity, F1 score, accuracy, and the area under the receiver operating characteristic curve (AUC). The experimental findings clearly demonstrated that the ResNet-50 model performed better than the VGG-16 and Alex Net models in terms of sensitivity, specificity, and accuracy.
Keywords: Concealed Weapon, Detector, Insecurity, Internet of Things.
Scope of the Article: Information Security