ShorelineNet: an efficient deep learning approach for shoreline semantic segmentation for unmanned surface vehicles

Yao, Linghong, Kanoulas, Dimitrios, Ji, Ze ORCID: https://orcid.org/0000-0002-8968-9902 and Liu, Yuanchang 2021. ShorelineNet: an efficient deep learning approach for shoreline semantic segmentation for unmanned surface vehicles. Presented at: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Prague, Czech Republic, 27 September - 01 October 2021. 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). pp. 5403-5409. 10.1109/IROS51168.2021.9636614

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Abstract

This paper introduces a novel deep learning approach to semantic segmentation of the shoreline environments with a high frames-per-second (fps) performance, making the approach readily applicable to autonomous navigation for Unmanned Surface Vehicles (USV). The proposed ShorelineNet is an efficient deep neural network of high performance relying only on visual input. ShorelineNet uses monocular visual input to produce accurate shoreline separation and obstacle detection compared to the state-of-the-art, and achieves this with real-time performance. Experimental validation on a challenging multi-modal maritime obstacle detection dataset, the MODD2 dataset, achieves a much faster inference (25fps on an NVIDIA Tesla K80 and 6fps on a CPU) with respect to the recent state-of-the-art methods, while keeping the performance equally high (73.1% F-score). This makes ShorelineNet a robust and effective model to be used for reliable USV navigation that require real-time and high-performance semantic segmentation of maritime environments.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Engineering
ISBN:	9781665417143
Date of First Compliant Deposit:	13 July 2021
Last Modified:	09 Nov 2022 11:16
URI:	https://orca.cardiff.ac.uk/id/eprint/142561

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