Robust firearm classification in RGB images: overcoming occlusion with compositional convolutional neural networks

Georgios Stavropoulos; Alexandros Kalpazidis; Konstantinos Votis

doi:10.1117/12.3031483

13 November 2024 Robust firearm classification in RGB images: overcoming occlusion with compositional convolutional neural networks

Georgios Stavropoulos, Alexandros Kalpazidis, Konstantinos Votis

Author Affiliations +

Proceedings Volume 13206, Artificial Intelligence for Security and Defence Applications II; 1320615 (2024) https://doi.org/10.1117/12.3031483
Event: Security + Defence, 2024, Edinburgh, United Kingdom

Conference Poster

Abstract

It is common for occlusion to occur in images relevant to counterterrorism applications tasked with firearm classification. To address this challenge, the architecture of a Compositional Convolutional Neural Network was selected for neural network construction. These networks, given appropriate training, demonstrate promising results in image classification even in the presence of occlusion. To adequately train the neural network while facing a shortage of available images depicting firearms under occlusion, a series of tools were developed to artificially introduce occlusion and noise. This facilitated the creation of an augmented dataset to complement the training dataset.

Citation Download Citation

Georgios Stavropoulos, Alexandros Kalpazidis, and Konstantinos Votis "Robust firearm classification in RGB images: overcoming occlusion with compositional convolutional neural networks", Proc. SPIE 13206, Artificial Intelligence for Security and Defence Applications II, 1320615 (13 November 2024); https://doi.org/10.1117/12.3031483

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available