Learning Deep Representations and Detection of Docking Stations Using Underwater Imaging

Abstract

Underwater docking endows AUVs with the ability of recharging and data transfer. Detection of underwater docking stations is a crucial step required to perform a successful docking. We propose a method to detect underwater docking stations using two dimensional images captured under different environmental light variance, deformations aroused by scale and rotation, different light intensity and partial observation. In order to realize our proposed method, we first train Convolutional Neural Networks (CNNs) to learn feature representations and then employ a deep detection network. In order to analyze the performance of the proposed method, we prepared an image dataset of docking stations using underwater imaging. Then, we explore the performance of our method using different data augmentation methods. We improved the AUC of detection by 0.14 using data augmentation and obtained 0.88 AUC with data augmentation. An increment of 0.23 AUC is gained by transfer learning and we obtained 0.88 AUC on another datasets.

Review of underwater visual navigation and docking: advances and challenges

“…In 2018, Liu [45] introduced an underwater docking site detection method employing Convolutional Neural Networks (CNNs). Two deep neural networks were utilized: Dock-net-C for site classification and Dock-net-D for site detection.…”