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Abstract
Obstacle detection is a fundamental capability of an autonomous maritime surface vessel (AMSV). State-of-the-art obstacle detection algorithms are based on convolutional neural networks (CNNs). While CNNs provide higher detection accuracy and fast detection speed, they require enormous amounts of data for their training. In particular, the availability of domain-specific datasets is a challenge for obstacle detection. The difficulty in conducting onsite experiments limits the collection of maritime datasets. Owing to the logistic cost of conducting on-site operations, simulation tools provide a safe and cost-efficient alternative for data collection. In this work, we introduce SimuShips, a publicly available simulation-based dataset for maritime environments. Our dataset consists of 9471 high-resolution (1920x1080) images which include a wide range of obstacle types, atmospheric and illumination conditions along with occlusion, scale and visible proportion variations. We provide annotations in the form of bounding boxes. In addition, we conduct experiments with YOLOv5 to test the viability of simulation data. Our experiments indicate that the combination of real and simulated images improves the recall for all classes by 2.9%.
Original language | English |
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Title of host publication | OCEANS 2022, Hampton Roads |
Publisher | IEEE |
Pages | 1-5 |
Number of pages | 5 |
ISBN (Print) | 978-1-6654-6810-7 |
DOIs | |
Publication status | Published - 2022 |
MoE publication type | A4 Article in a conference publication |
Event | OCEANS - Duration: 17 Oct 2022 → … |
Publication series
Name | Oceans |
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ISSN (Print) | 0197-7385 |
Conference
Conference | OCEANS |
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Period | 17/10/22 → … |
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Dive into the research topics of 'SimuShips - A High Resolution Simulation Dataset for Ship Detection with Precise Annotations'. Together they form a unique fingerprint.Projects
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EDISS: EMJMD Programme on the Engineering of Data-intensive Intelligent Software Systems
Lafond, S. (Principal Investigator), Azimi Rashti, S. (Principal Investigator), Lilius, J. (Co-Principal Investigator), Strömborg, M. (Coordinator) & Iancu, B. (Co-Investigator)
Education, Audiovisual and Culture Executive Agency - European Commission
01/09/20 → 31/08/26
Project: EU