Model for Hidden Weapon Detection Using Deep Convolutional Neural Network

Moradeke Grace Adewumi, Olumide Sunday Adewale and Bolanle A. Ojokoh
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Moradeke Grace Adewumi: Department of Computing and Information Science, Bamidele Olumilua University of Education Science and Technology, Ikere Ekiti State, Nigeria.
Olumide Sunday Adewale: Department of Computer Science, Federal University of Technology, Akure. Ondo State, Nigeria.
Bolanle A. Ojokoh: Department of Computer Science, Federal University of Technology, Akure. Ondo State, Nigeria.

International Journal of Latest Technology in Engineering, Management & Applied Science, 2024, vol. 13, issue 11, 124-129

Abstract: Insecurity has been a major threat to government and civilians in Nigeria for the past decade. Development of a security system is not yet enough to curb the situation. Hence, the need for weapon detection using Convolutional Neural Network. The researchers downloaded different images with guns and knives from the internet. Image labeler software was used to annotate each image separately and the results were saved as XML files. This was converted to CSV files which are represented in form of rows and columns. Rows are each element, while the column are the weight, height, Xmin, Ymin, Xmax and Ymax. Which represent the shape and location of the boxes. Extra files were created which was mapped to a particular number, and the label was represented in form of numbers such as 1 for knife and 0 for gun. TensorFlow API was used for the training. We trained 300epochs at 0.03 learning rate for Resnet50, Resnet101, InceptionV1 and the proposed model. The success rate of the training was determined, and the trained model was tested. The proposed model performed better than three other models when trained and tested with the same datasets.

Date: 2024
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