Self-learning Framework with Temporal Filtering for Robust Maritime Vessel Detection

Ghahremani, Amir; Bondarev, Egor

doi:10.1007/978-3-030-19816-9_10

Cited by 2 publications

(3 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this subsection, we compare the SSD detector (pre-trained on vessel images from our previous work [4]) against the proposed cascaded-SSD detector. B.1.…”

Section: B Validation Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Cascaded CNN Method for Far Object Detection in Outdoor Surveillance

Ghahremani¹,

Bondarev²

2018

2018 14th International Conference on Signal-Image Technology &Amp; Internet-Based Systems (SITIS)

Self Cite

View full text Add to dashboard Cite

DOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User Agreement:

show abstract

“…In this subsection, we compare the SSD detector (pre-trained on vessel images from our previous work [4]) against the proposed cascaded-SSD detector. B.1.…”

Section: B Validation Resultsmentioning

confidence: 99%

“…In our experiments, we re-use the vessel model obtained in our previous work [4] as the trained object model for the SSD. Then, the data flow continues in two separate paths.…”

Section: A System Overviewmentioning

confidence: 99%

Cascaded CNN Method for Far Object Detection in Outdoor Surveillance

Ghahremani¹,

Bondarev²

2018

2018 14th International Conference on Signal-Image Technology &Amp; Internet-Based Systems (SITIS)

Self Cite

View full text Add to dashboard Cite

show abstract

“…Botlek Dataset [263] is a dataset containing an image set sampled from video recordings (6 view points) from the Botlek region in the port of Rotterdam, Netherlands. The dataset captures a variety of weather conditions, object sizes, camera positions, occlusions, etc.…”

Section: Generic Sod Datasetsmentioning

confidence: 99%

A Guide to Image and Video based Small Object Detection using Deep Learning : Case Study of Maritime Surveillance

Rekavandi¹,

Liu²,

Boussaid³

et al. 2022

Preprint

View full text Add to dashboard Cite

Small object detection (SOD) in optical images and videos is a challenging problem that even state-of-the-art generic object detection methods fail to accurately localize and identify such objects. Typically, small objects appear in real-world due to large camera-object distance. Because small objects occupy only a small area in the input image (e.g., less than 10%), the information extracted from such a small area is not always rich enough to support decision making. Multidisciplinary strategies are being developed by researchers working at the interface of deep learning and computer vision to enhance the performance of SOD deep learning based methods. In this paper, we provide a comprehensive review of over 160 research papers published between 2017 and 2022 in order to survey this growing subject. This paper summarizes the existing literature and provide a taxonomy that illustrates the broad picture of current research. We investigate how to improve the performance of small object detection in maritime environments, where increasing performance is critical. By establishing a connection between generic and maritime SOD research, future directions have been identified. In addition, the popular datasets that have been used for SOD for generic and maritime applications are discussed, and also well-known evaluation metrics for the state-of-the-art methods on some of the datasets are provided.

show abstract

Self-learning Framework with Temporal Filtering for Robust Maritime Vessel Detection

Cited by 2 publications

References 12 publications

Cascaded CNN Method for Far Object Detection in Outdoor Surveillance

Cascaded CNN Method for Far Object Detection in Outdoor Surveillance

A Guide to Image and Video based Small Object Detection using Deep Learning : Case Study of Maritime Surveillance

Contact Info

Product

Resources

About