Abstract:
Early detection of armed threats is crucial in
reducing accidents and deaths resulting from armed conflicts and
terrorist attacks. The most significant application of weapon
detection systems would be found in public areas such as
airports, stadiums, central squares, and on the battlefield in
urban or rural conditions. Modern surveillance and control
systems of closed-circuit television cameras apply deep learning
and machine learning algorithms for weapons detection on the
base of cloud architecture. However, cloud computing is
inefficient for network bandwidth, data privacy and slow
decision-making. To address these issues, edge computing can be
applied, using Raspberry Pi as an edge device with the
EfficientDet model for developing the weapons detection system.
The image processing results are transmitted as a text report to
the cloud platform for further analysis by the operator. Soldiers
can equip themselves with the suggested edge node and
headphones for armed threat notifications, plugged into
augmented reality glasses for visual data output. As a result, the
application of edge computing makes it possible to ensure data
safety, increase the network bandwidth and provide the device
operation without the internet. Thus, an independent weapon
detection system was developed that identifies weapons in 1.48
seconds without the Internet.
