Weapon and gun violence have recently become a pressing issue today. The
degree of these crimes and activities has risen to the point of being termed as
an epidemic. This prevalent misuse of weapons calls for an automatic system
that detects weapons in real-time. Real-time surveillance video is captured and
recorded in almost all public forums and places. These videos contain abundant
raw data which can be extracted and processed into meaningful information. This
paper proposes a novel pipeline consisting of an ensemble of convolutional
neural networks with distinct architectures. Each neural network is trained
with a unique mini-batch with little to no overlap in the training samples.
This paper will present several promising results using multiple datasets
associated with comparing the proposed architecture and state-of-the-art (SoA)
models. The proposed pipeline produced an average increase of 5% in accuracy,
specificity, and recall compared to the SoA systems.

提出了一种新的流程，该流程由一组具有不同架构的卷积神经网络组成，用于监测实时视频中的武器，相比现有系统，该流程平均提高了 5% 的准确性、特异性和召回率。