Firearm detection using DETR with multiple self-coordinated neural networks

Abstract

This paper presents a new strategy that uses multiple neural networks in conjunction with the DEtection TRansformer (DETR) network to detect firearms in surveillance images. The strategy developed in this work presents a methodology that promotes collaboration and self-coordination of networks in the fully connected layers of DETR through the technique of multiple self-coordinating artificial neural networks (MANN), which does not require a coordinator. This self-coordination consists of training the networks one after the other and integrating their outputs without an extra element called a coordinator. The results indicate that the proposed network is highly effective, achieving high-level outcomes in firearm detection. The network’s high precision of 84% and its ability to perform classifications are noteworthy.