Learning to Detect Subsea Pipelines with Deep Segmentation Network and Self-Supervision

Regular inspection of subsea pipelines is crucial for assessing their integrity and for maintenance. These inspections usually are very expensive without employing Autonomous Underwater Vehicles (AUVs). Most of the research focus in this area has been directed in automating the process to reduce operational costs and is done by using multiple perceptive sensors. This paper investigates the problem of pipeline detection using optical sensors in highly turbid subsea scenarios. A deep neural network is designed to segment pipes from optical images. Since a common issue with underwater optical sensing is dynamic changes in scenes and the difficulty of labelling large dataset, a novel self-supervision method based on multibeam echosounder is proposed to fine-tune a pre-trained network on the fly. Extensive experiments are conducted in real-world challenging scenarios, showing the effectiveness of the proposed method. The proposed method can run real-time on an Nvidia Jetson AGX embedded PC, supporting AUV field operation.