Deep Learning-Based Maritime Environment Segmentation for Unmanned Surface Vehicles Using Superpixel Algorithms

Xue, Haolin1; Chen, Xiang2; Zhang, Ruo3,4; Wu, Peng1; Li, Xudong5,6; Liu, Yuanchang1

2021-12-01

10.3390/jmse9121329

JOURNAL OF MARINE SCIENCE AND ENGINEERING   影响因子和分区

2077-1312

Unmanned surface vehicles (USVs) are receiving increasing attention in recent years from both academia and industry. To make a high-level autonomy for USVs, the environment situational awareness is a key capability. However, due to the richness of the features in marine environments, as well as the complexity of the environment influenced by sun glare and sea fog, the development of a reliable situational awareness system remains a challenging problem that requires further studies. This paper, therefore, proposes a new deep semantic segmentation model together with a Simple Linear Iterative Clustering (SLIC) algorithm, for an accurate perception for various maritime environments. More specifically, powered by the SLIC algorithm, the new segmentation model can achieve refined results around obstacle edges and improved accuracy for water surface obstacle segmentation. The overall structure of the new model employs an encoder-decoder layout, and a superpixel refinement is embedded before final outputs. Three publicly available maritime image datasets are used in this paper to train and validate the segmentation model. The final output demonstrates that the proposed model can provide accurate results for obstacle segmentation.

unmanned surface vehicles image segmentation deep convolutional neural network superpixel algorithm maritime image data

SCI

英语

其他

Royal Society[IEC-NSFC-191633]

Engineering ; Oceanography

Engineering, Marine ; Engineering, Ocean ; Oceanography

WOS:000738088300001

MDPI

Web of Science

1.UCL, Dept Mech Engn, Torrington Pl, London WC1E 7JE, England
2.UCL, Dept Civil Environm & Geomat Engn, Chadwick Bldg, London WC1E 6BT, England
3.Chinese Univ Hong Kong, Shenzhen Res Inst, Shenzhen 518057, Peoples R China
4.Southern Univ Sci & Technol, Dept Elect & Elect Engn, Shenzhen 518055, Peoples R China
5.Dalian Univ Technol, Sch Mech Engn, Dalian 116024, Peoples R China
6.Dalian Univ Technol, Sch Mech Engn, Key Lab Micro Nano Technol & Syst Liaoning Prov, Dalian 116024, Peoples R China

Xue, Haolin,Chen, Xiang,Zhang, Ruo,et al. Deep Learning-Based Maritime Environment Segmentation for Unmanned Surface Vehicles Using Superpixel Algorithms[J]. JOURNAL OF MARINE SCIENCE AND ENGINEERING,2021,9(12).

Xue, Haolin,Chen, Xiang,Zhang, Ruo,Wu, Peng,Li, Xudong,&Liu, Yuanchang.(2021).Deep Learning-Based Maritime Environment Segmentation for Unmanned Surface Vehicles Using Superpixel Algorithms.JOURNAL OF MARINE SCIENCE AND ENGINEERING,9(12).

Xue, Haolin,et al."Deep Learning-Based Maritime Environment Segmentation for Unmanned Surface Vehicles Using Superpixel Algorithms".JOURNAL OF MARINE SCIENCE AND ENGINEERING 9.12(2021).