Improving Drone Imagery For Computer Vision/Machine Learning in
  Wilderness Search and Rescue
Robin Murphy Thomas Manzini
This paper describes gaps in acquisition of drone imagery that impair the use
with computer vision/machine learning (CV/ML) models and makes five
recommendations to maximize image suitability for CV/ML post-processing. It
describes a notional work process for the use of drones in wilderness search
and rescue incidents. The large volume of data from the wide area search phase
offers the greatest opportunity for CV/ML techniques because of the large
number of images that would otherwise have to be manually inspected. The 2023
Wu-Murad search in Japan, one of the largest missing person searches conducted
in that area, serves as a case study. Although drone teams conducting wide area
searches may not know in advance if the data they collect is going to be used
for CV/ML post-processing, there are data collection procedures that can
improve the search in general with automated collection software. If the drone
teams do expect to use CV/ML, then they can exploit knowledge about the model
to further optimize flights.