Self-learning framework with temporal filtering for robust maritime vessel detection

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Abstract

With the recent development in ConvNet-based detectors, a successful solution for vessel detection becomes possible. However, it is essential to access a comprehensive annotated training set from different maritime environments. Creating such a dataset is expensive and time consuming. To automate this process, this paper proposes a novel self learning framework which automatically finetunes a generic pre-trained model to any new environment. With this, the framework enables automated labeling of new dataset types. The method first explores the video frames captured from a new target environment to generate the candidate vessel samples. Afterwards, it exploits a temporal filtering concept to verify the correctly generated candidates as new labels for learning, while removing the false positives. Finally, the system updates the vessel model using the provided self-learning dataset. Experimental results on our real-world evaluation dataset show that generalizing a finetuned Single Shot Detector to a new target domain using the proposed self-learning framework increases the average precision and the F1-score by 12% and 5%, respectively. Additionally, the proposed temporal filter reduced the noisy detections in a sensitive setting from 58% to only 5%.

Original languageEnglish
Title of host publicationRepresentations, Analysis and Recognition of Shape and Motion from Imaging Data - 7th International Workshop, RFMI 2017, Revised Selected Papers
EditorsFaouzi Ghorbel, Liming Chen, Boulbaba Ben Amor
Place of PublicationCham
PublisherSpringer
Pages121-135
Number of pages15
ISBN (Electronic)978-3-030-19816-9
ISBN (Print)978-3-030-19815-2
DOIs
Publication statusPublished - 5 May 2019
Event7th International Workshop on Representations, Analysis and Recognition of Shape and Motion from Imaging Data, RFMI 2017 - Savoie, France
Duration: 17 Dec 201720 Dec 2017

Publication series

NameCommunications in Computer and Information Science
Volume842
ISSN (Print)1865-0929

Conference

Conference7th International Workshop on Representations, Analysis and Recognition of Shape and Motion from Imaging Data, RFMI 2017
Country/TerritoryFrance
CitySavoie
Period17/12/1720/12/17

Funding

Acknowledgement. This work is supported by the European ITEA APPS project. We thank the company Vinotion for providing the Botlek dataset to us. We also show our gratitude to the company NVIDIA for granting us a “TITAN X PASCAL” GPU.

Keywords

  • Automated dataset creation
  • ConvNet
  • Convolutional networks (CNN)
  • Maritime surveillance
  • Self-learning
  • Vessel detection

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