  This paper details the challenges in applying two computer vision systems, an
EfficientDET supervised learning model and the unsupervised RX spectral
classifier, to 98.9 GB of drone imagery from the Wu-Murad wilderness search and
rescue (WSAR) effort in Japan and identifies 3 directions for future research.
There have been at least 19 proposed approaches and 3 datasets aimed at
locating missing persons in drone imagery, but only 3 approaches (2
unsupervised and 1 of an unknown structure) are referenced in the literature as
having been used in an actual WSAR operation. Of these proposed approaches, the
EfficientDET architecture and the unsupervised spectral RX classifier were
selected as the most appropriate for this setting. The EfficientDET model was
applied to the HERIDAL dataset and despite achieving performance that is
statistically equivalent to the state-of-the-art, the model fails to translate
to the real world in terms of false positives (e.g., identifying tree limbs and
rocks as people), and false negatives (e.g., failing to identify members of the
search team). The poor results in practice for algorithms that showed good
results on datasets suggest 3 areas of future research: more realistic datasets
for wilderness SAR, computer vision models that are capable of seamlessly
handling the variety of imagery that can be collected during actual WSAR
operations, and better alignment on performance measures.
