  Each year, underwater remotely operated vehicles (ROVs) collect thousands of
hours of video of unexplored ocean habitats revealing a plethora of information
regarding biodiversity on Earth. However, fully utilizing this information
remains a challenge as proper annotations and analysis require trained
scientists time, which is both limited and costly. To this end, we present a
Dataset for Underwater Substrate and Invertebrate Analysis (DUSIA), a benchmark
suite and growing large-scale dataset to train, validate, and test methods for
temporally localizing four underwater substrates as well as temporally and
spatially localizing 59 underwater invertebrate species. DUSIA currently
includes over ten hours of footage across 25 videos captured in 1080p at 30 fps
by an ROV following pre planned transects across the ocean floor near the
Channel Islands of California. Each video includes annotations indicating the
start and end times of substrates across the video in addition to counts of
species of interest. Some frames are annotated with precise bounding box
locations for invertebrate species of interest, as seen in Figure 1. To our
knowledge, DUSIA is the first dataset of its kind for deep sea exploration,
with video from a moving camera, that includes substrate annotations and
invertebrate species that are present at significant depths where sunlight does
not penetrate. Additionally, we present the novel context-driven object
detector (CDD) where we use explicit substrate classification to influence an
object detection network to simultaneously predict a substrate and species
class influenced by that substrate. We also present a method for improving
training on partially annotated bounding box frames. Finally, we offer a
baseline method for automating the counting of invertebrate species of
interest.
