Machine learning for shipwreck segmentation from side scan sonar imagery: Dataset and benchmark

Advaith Sethuraman, Anja Sheppard, Onur Bagoren, Christopher Pinnow, Jamey Anderson, Timothy Havens, Katherine A. Skinner

Abstract:

Open-source benchmark datasets have been a critical component for advancing machine learning for robot
perception in terrestrial applications. Benchmark datasets enable the widespread development of state-of-the-art
machine learning methods, which require large datasets for training, validation, and thorough comparison to
competing approaches. Underwater environments impose several operational challenges that hinder efforts to
collect large benchmark datasets for marine robot perception. Furthermore, a low abundance of targets of interest
relative to the size of the search space leads to increased time and cost required to collect useful datasets for a
specific task. As a result, there is limited availability of labeled benchmark datasets for underwater applications.
We present the AI4Shipwrecks dataset, which consists of 28 distinct shipwrecks totaling 286 high-resolution labeled
side scan sonar images to advance the state-of-the-art in autonomous sonar image understanding. We leverage the
unique abundance of targets in Thunder Bay National Marine Sanctuary in Lake Huron, MI, to collect and compile
a sonar imagery benchmark dataset through surveys with an autonomous underwater vehicle (AUV). We consulted
with expert marine archaeologists for the labeling of robotically gathered data. We then leverage this dataset to
perform benchmark experiments for comparison of state-of-the-art supervised segmentation methods, and we
present insights on opportunities and open challenges for the field. The dataset and benchmarking tools will be
released as an open-source benchmark dataset to spur innovation in machine learning for Great Lakes and ocean
exploration. The dataset and accompanying software are available at https://umfieldrobotics.github.io/
ai4shipwrecks/.