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Evaluating deep semantic segmentation networks for object detection in maritime surveillance

Cane, T. and Ferryman, J. (2018) Evaluating deep semantic segmentation networks for object detection in maritime surveillance. In: 15th IEEE International Conference on Advanced Video and Signal-based Surveillance, 27-30 Nov 2018, Auckland, New Zealand, pp. 1-6.

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Official URL: https://ieeexplore.ieee.org/document/8639077

Abstract/Summary

Maritime surveillance is important for applications in safety and security, but the visual detection of objects in maritime scenes remains challenging due to the diverse and unconstrained nature of such environments, and the need to operate in near real-time. Recent work on deep neural networks for semantic segmentation has achieved good performance in the road/urban scene parsing task. Driven by the potential application in autonomous vehicle navigation, many of the architectures are designed to be fast and lightweight. In this paper, we evaluate semantic segmentation networks in the context of an object detection system for maritime surveillance. Using data from the ADE20k scene parsing dataset, we train a selection of recent semantic segmentation network architectures to compare their performance on a number of publicly available maritime surveillance datasets.

Item Type:Conference or Workshop Item (Paper)
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:81556

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