Improving Drone Imagery For Computer Vision/Machine Learning in Wilderness Search and Rescue

TL;DR

This paper identifies gaps in drone imagery collection for wilderness search and rescue, offering five recommendations to improve data quality for computer vision and machine learning applications, exemplified by a case study in Japan.

Contribution

It proposes specific data collection procedures and strategies to enhance drone imagery for CV/ML in search and rescue, including optimizing flights based on model knowledge.

Findings

Large data volume from wide area searches offers significant CV/ML opportunities.

Automated data collection procedures can improve search efficiency.

Case study demonstrates practical application in Japan.

Abstract

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.