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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.

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Metadaten
Author:Dennis Griesser, 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
Relevance:Keine peer reviewed Publikation (Wissenschaftlicher Artikel und Aufsatz, Proceeding, Artikel in Tagungsband)
Open Access?:Nein
Licence (German):License LogoUrheberrechtlich geschützt