  With the end goal of selecting and using diver detection models to support
human-robot collaboration capabilities such as diver following, we thoroughly
analyze a large set of deep neural networks for diver detection. We begin by
producing a dataset of approximately 105,000 annotated images of divers sourced
from videos -- one of the largest and most varied diver detection datasets ever
created. Using this dataset, we train a variety of state-of-the-art deep neural
networks for object detection, including SSD with Mobilenet, Faster R-CNN, and
YOLO. Along with these single-frame detectors, we also train networks designed
for detection of objects in a video stream, using temporal information as well
as single-frame image information. We evaluate these networks on typical
accuracy and efficiency metrics, as well as on the temporal stability of their
detections. Finally, we analyze the failures of these detectors, pointing out
the most common scenarios of failure. Based on our results, we recommend SSDs
or Tiny-YOLOv4 for real-time applications on robots and recommend further
investigation of video object detection methods.
