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

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.