  There has been an extensive use of Unmanned Aerial Vehicles in search and
rescue missions to distribute first aid kits and food packets. It is important
that these UAVs are able to identify and distinguish the markers from one
another for effective distribution. One of the common ways to mark the
locations is via the use of characters superimposed on shapes of various colors
which gives rise to wide variety of markers based on combination of different
shapes, characters, and their respective colors.
  In this paper, we propose an object detection and classification pipeline
which prevents false positives and minimizes misclassification of alphanumeric
characters and shapes in aerial images. Our method makes use of traditional
computer vision techniques and unsupervised machine learning methods for
identifying region proposals, segmenting the image targets and removing false
positives. We make use of a computationally light model for classification,
making it easy to be deployed on any aerial vehicle.
