Dong, Yuxuan;
Wu, Peng;
Wang, Sen;
Liu, Yuanchang;
(2023)
ShipGAN: Generative Adversarial Network based simulation-to-real image translation for ships.
Applied Ocean Research
, 131
, Article 103456. 10.1016/j.apor.2022.103456.
(In press).
Preview |
Text
ShipGAN.pdf - Published Version Download (8MB) | Preview |
Abstract
Recent advances in robotics and autonomous systems (RAS) have significantly improved the autonomy level of unmanned surface vehicles (USVs) and made them capable of undertaking demanding tasks in various environments. During the operation of USVs, apart from normal situations, it is those unexpected scenes, such as busy waterways or navigation in dust/nighttime, impose most dangers to USVs as these scenes are rarely seen during training. Such a rare occurrence also makes the manual collection and recording of these scenes into dataset difficult, expensive and inefficient, with the majority of existing public available datasets not able to fully cover them. One of many plausible solutions is to purposely generate these data using computer vision techniques with the assistance from high-fidelity simulations that can create various desirable motions/scenarios. However, the stylistic difference between the simulation images and the natural images would cause a domain shift problem. Hence, there is a need for designing a method that can transfer the data distribution and styles of the simulation images into the realistic domain. This paper proposes and evaluates a novel solution to fill this gap using a Generative Adversarial Network (GAN) based model, ShipGAN, to translate the simulation images into realistic images. Experiments were carried out to investigate the feasibility of generating realistic images using GAN-based image translation models. The synthetic realistic images from the simulation images were demonstrated to be reliable by the object detection and image segmentation algorithms trained with natural images.
| Type: | Article |
|---|---|
| Title: | ShipGAN: Generative Adversarial Network based simulation-to-real image translation for ships |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.apor.2022.103456 |
| Publisher version: | https://doi.org/10.1016/j.apor.2022.103456 |
| Language: | English |
| Additional information: | This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third-party material in this article are included in the Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
| Keywords: | Generative networks, Sim-to-real translation, Autonomous navigation, Unmanned surface vessels |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Mechanical Engineering |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10163137 |
Archive Staff Only
 |
View Item |
