Seafloor Pipeline Detection With Deep Learning - Schøyen, Vemund Sigmundson; Warakagoda, Narada Dilp; Midtgaard, Øivind (Proceedings of the Northern Lights Deep Learning Workshop, 2021)
This paper presents fast, accurate, and automatic methods for detecting seafloor pipelines in multibeam echo sounder data with deep learning. The proposed methods take inspiration from the highly successful ResNet and YOLO deep learning models and tailor them to the idiosyncrasies of the seafloor pipeline detection task.We use the area between lines and Hausdorff line distance functions to accurately evaluate how well methods can localize (pipe)lines. The same functions also show promise as loss functions compared to standard mean squared error, which does not include the regression variables' geometrical interpretation.The model outperforms the highest likelihood baseline by more than 35% on a region-wise F1-score classification evaluation while being more than eight times more accurate than the baseline in locating pipelines. It is efficient, operating at over eighteen 32-ping image segments per second, which is far beyond real-time requirements.
Keywords: Object detection; Seafloor pipelines; Autonomous Underwater Vehicle; Deep learning; Multibeam Echo Sounder; Hausdorff Line Distance