Geodesic
The classification of firing pin impressions using the Convolutional Neural Network (CNN)
Informacionnye tehnologii i vyčislitelnye sistemy, no. 1 (2024), pp. 87-96.

Voir la notice de l'article provenant de la source Math-Net.Ru

The article discusses the possibility of classification of images of Firing Pin Impressions with the use of Convolutional Neural Network (CNN). The aim of the work is to investigate the effectiveness of CNN for multiclass classification of Firing Pin impressions for several firearms. The scientific novelty of the research is in the development of the CNN for the classification of Firing Pin Impressions under the condition of a small number of source objects used for the CNN training (only 4 images for each class). In order to prove the effectiveness of the CNN training the augmented training database was prepared. For this purpose, each source image in the training database was cloned and eight new images with limited modifications were made. The results of the examination of developed CNN with the database including 40 different classes (firearms) show that the accuracy is about 93% if only one maximal result is considered. In case of considering three highest results, the accuracy increases to 97–98%. The presented work can be of interest for developers of software for automatic ballistic identification system and for firearms examiners of regional forensic ballistic laboratories working with digital microscopes.
Mots-clés : Firing Pin Impressions, Convolutional Neural Network, multiclass classification, augmentation, firearms identification. 
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     author = {V. A. Fedorenko and K. O. Sorokina and P. V. Giverts},
     title = {The classification of firing pin impressions using the {Convolutional} {Neural} {Network} {(CNN)}},
     journal = {Informacionnye tehnologii i vy\v{c}islitelnye sistemy},
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V. A. Fedorenko; K. O. Sorokina; P. V. Giverts. The classification of firing pin impressions using the Convolutional Neural Network (CNN). Informacionnye tehnologii i vyčislitelnye sistemy, no. 1 (2024), pp. 87-96. https://geodesic-test.mathdoc.fr/item/ITVS_2024_1_a8/