Marine Applied Research and Exploration (MARE) has collected hundreds of hours of video using their unmanned, underwater, remote-operated vehicles (ROVs). In order to better survey and understand life in California's costal waters, MARE has annotated each video with species and substrate labels.
As a first step toward creating a ontext-Driven Detector for underwater species, we first implemented a method using a convolutional neural network (CNN) capable of generating temporal labels for DUSIA. A ResNet-based classification models classifies the video frame by frame.
.mp4 video
.csv with predicted substrate for each frame in the video