Paper Info
Title
Collision-Free Waterway Segmentation for Inland Unmanned Surface Vehicles
Abstract
Accurate detection of passable regions in images is important for ensuring the safe navigation of unmanned surface vehicles, especially in inland waterways with irregular waterlines and various obstacles. However, the existing methods are susceptible to environmental changes and produce high false-positive rates (FPRs) for confusable textures and complex edge details. We, therefore, propose a collision-free waterway segmentation network based on deep learning such that pixel-level classification results can be obtained. The segmentation accuracy for indistinguishable textures is improved by learning the context dependency of features through a modified context prior, and the detailed refinement of waterlines and small obstacles is achieved via an asymmetric encoder-decoder structure. To learn the features of waterways as comprehensively as possible, data integration and data augmentation are performed on three public datasets. In addition, a new annotated urban waterway dataset called the Dasha River dataset is proposed and made publicly available. The proposed model is tested and cross validated using multiple inland and maritime water segmentation datasets, the results of which show that the model achieves superior performance than the current state of the art with pixel accuracy (PA) of 97.43% and FPR of 1.37%.
Year
DOI
Venue
2022
10.1109/TIM.2022.3165803
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
Keywords
DocType
Volume
Image segmentation, Feature extraction, Image edge detection, Rivers, Deep learning, Sea surface, Training, Collision-free waterway segmentation, Dasha River dataset, deep learning, inland waterway, unmanned surface vehicles (USVs)
Journal
71
ISSN
Citations 
PageRank 
0018-9456
0
0.34
References 
Authors
0
8
Name
Order
Citations
PageRank
Rundong Zhou100.34
Yulong Gao203.72
Wu Peng36724.15
Xiongwei Zhao400.34
Wenhao Dou500.34
Chenyang Sun600.34
Yu Zhong700.34
Wang Yang84612.09