CNN-Based Monocular 3D Ship Detection Using Inverse Perspective
- Three-dimensional ship localization with only one camera is a challenging task due to the loss of depth information caused by perspective projection. In this paper, we propose a method to measure distances based on the assumption that ships lie on a flat surface. This assumption allows to recover depth from a single image using the principle of inverse perspective. For the 3D ship detection task, we use a hybrid approach that combines image detection with a convolutional neural network, camera geometry and inverse perspective. Furthermore, a novel calculation of object height is introduced. Experiments show that the monocular distance computation works well in comparison to a Velodyne lidar. Due to its robustness, this could be an easy-to-use baseline method for detection tasks in navigation systems.
| Author: | Dennis GriesserORCiD, Daniel Dold, Georg UmlaufORCiDGND, Matthias O. FranzORCiDGND |
|---|---|
| DOI: | https://doi.org/10.1109/IEEECONF38699.2020.9389028 |
| ISBN: | 978-1-7281-8409-8 |
| ISBN: | 978-1-7281-5446-6 |
| Parent Title (English): | Global Oceans 2020, Oceans Conference and Exposition, October 5th - 30th 2020, virtual |
| Publisher: | IEEE |
| Document Type: | Conference Proceeding |
| Language: | English |
| Year of Publication: | 2020 |
| Release Date: | 2021/01/15 |
| Tag: | Inverse perspective; Mask R-CNN; 3D ship detection; Ship dataset |
| Page Number: | 8 |
| Institutes: | Institut für Optische Systeme - IOS |
| Open Access?: | Nein |
| Licence (German): |  Urheberrechtlich geschützt |
