Water-obstacle segmentation based on Swin transformer and sea-sky-line detection

Ziyi Zhang; Zijian Xing; Xiangfei Qiu; Yunong Leng; YanRong Kuang

doi:10.1117/12.2639502

27 June 2022 Water-obstacle segmentation based on Swin transformer and sea-sky-line detection

Ziyi Zhang, Zijian Xing, Xiangfei Qiu, Yunong Leng, YanRong Kuang

Proceedings Volume 12253, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2022); 122531O (2022) https://doi.org/10.1117/12.2639502
Event: Second International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2022), 2022, Qingdao, China

Abstract

Unmanned surface vehicles (USV) has been widely used in the fields of surface quality monitoring and dangerous water exploration, among which the detection and segmentation of obstacles is an important condition for USV to realize autonomous obstacle avoidance. Recently, most of the segmentation methods of water surface obstacles are based on CNN, but due to the limited receptive field of CNN, most of the methods do not achieve satisfactory results in the segmentation of obstacle edge details, sea-sky segmentation and surface reflection. To solve the above problems, we propose a water surface obstacle segmentation network WSSS with sea-sky-line detection as the prior knowledge for feature fusion, Swin Transformer as the dominant backbone and feature pyramid network (FPN) as the feature fusion network. In this network, sea-sky-line detection is used as a preprocessing module to increase the receptive field through the self-attention mechanism and sliding window, and feature extraction is enhanced by FPN, which makes the network segmentation of obstacle edge and water Sky more detailed and reduces the influence caused by reflection and weather. Extensive experiments on the USV datasets MaSTr1325 and Modd2 show that the proposed method WSSS outperforms the current state-of-the-art methods for water surface obstacle segmentation.

Citation Download Citation

Ziyi Zhang, Zijian Xing, Xiangfei Qiu, Yunong Leng, and YanRong Kuang "Water-obstacle segmentation based on Swin transformer and sea-sky-line detection", Proc. SPIE 12253, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2022), 122531O (27 June 2022); https://doi.org/10.1117/12.2639502

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
6 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Image segmentation

Transformers

Water

Feature extraction

Image fusion

Antennas

Network architectures

Show All Keywords

Keywords/Phrases

Search In:

Publication Years