A Hybrid Deep Learning Framework Combining Swin Transformer and Mask R- CNN for Dismantled Weapon Part Detection

The growing prevalence of dangerous weapons
being misused in public spaces presents a major danger to the
public. As a result, society needs intelligent surveillance systems
so that early identification of threats can occur. The challenge
with identifying dismantled weapon parts is based upon their

minimal size, ability to be hidden, and their likeness to other non-
threatening objects. The current methods of detecting weapons are

primarily based on only identifying assembled weapons and are
restricted to the limits of conventional CNN-based architecture.
These limitations of current solutions result in no contextual
understanding of the surrounding environment and minimal
effectiveness in identifying threats that are occluded or
surrounded by cluttered areas. This paper proposes a framework

for the identification of dismantled weapon parts using a Mask R-
CNN architecture, combined with a Swin Transformer, as the

backbone of the framework. The Swin Transformer allows for the
extraction of hierarchical multi-scale features using shifted
window self-attention. Through the Mask R-CNN architecture,
instance-level classification, bounding box regression, and
accurate segmentation of each weapon component are possible.
The results of the experiments demonstrate that this system
performs exceptionally well: it has demonstrated a 96.8%
accuracy, 95.9% precision, and 96.3% recall. Moreover, this
system supports proactive identification of threats, allowing police
to take more effective steps toward keeping the public safe.