  Now a days, UAVs such as drones are greatly used for various purposes like
that of capturing and target detection from ariel imagery etc. Easy access of
these small ariel vehicles to public can cause serious security threats. For
instance, critical places may be monitored by spies blended in public using
drones. Study in hand proposes an improved and efficient Deep Learning based
autonomous system which can detect and track very small drones with great
precision. The proposed system consists of a custom deep learning model Tiny
YOLOv3, one of the flavors of very fast object detection model You Look Only
Once (YOLO) is built and used for detection. The object detection algorithm
will efficiently the detect the drones. The proposed architecture has shown
significantly better performance as compared to the previous YOLO version. The
improvement is observed in the terms of resource usage and time complexity. The
performance is measured using the metrics of recall and precision that are 93%
and 91% respectively.
