  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.
