Underwater Dock Detection through Convolutional Neural Networks Trained with Artificial Image Generation

Abstract

Autonomous Underwater Vehicles (AUVs) are a vital element for ocean exploration in various applications; however, energy sustainability still limits long-term operations. An option to overcome this problem is using underwater docking for power and data transfer. To robustly guide an AUV into a docking station, we propose an underwater vision algorithm for short-distance detection. In this paper, we present a Convolutional Neural Network architecture to accurately estimate the dock position during the terminal homing stage of the docking. Additionally, to alleviate the lack of available underwater datasets, two methods are proposed to generate synthetic datasets, one using a CycleGAN network, and another using Artistic Style transfer network. Both methods are used to train the same CNN architecture to compare the results. Finally, implementation details of the CNN are presented under the backseat architecture and ROS framework, running on an IVER3 AUV.