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Feature Pyramid U-Net with Attention for Semantic Segmentation of Forward-Looking Sonar Images

文献类型：期刊论文

作者Zhao, Dongdong3; Ge, Weihao3; Chen, Peng3; Hu, Yingtian2; Dang, Yuanjie3; Liang, Ronghua3; Guo, Xinxin1
刊名SENSORS
出版日期2022-11-01
卷号22期号:21页码:22
关键词forward-looking sonar sonar image segmentation semantic segmentation attention mechanism convolution neural network
DOI10.3390/s22218468
通讯作者Chen, Peng(chenpeng@zjut.edu.cn)
英文摘要Forward-looking sonar is a technique widely used for underwater detection. However, most sonar images have underwater noise and low resolution due to their acoustic properties. In recent years, the semantic segmentation model U-Net has shown excellent segmentation performance, and it has great potential in forward-looking sonar image segmentation. However, forward-looking sonar images are affected by noise, which prevents the existing U-Net model from segmenting small objects effectively. Therefore, this study presents a forward-looking sonar semantic segmentation model called Feature Pyramid U-Net with Attention (FPUA). This model uses residual blocks to improve the training depth of the network. To improve the segmentation accuracy of the network for small objects, a feature pyramid module combined with an attention structure is introduced. This improves the model's ability to learn deep semantic and shallow detail information. First, the proposed model is compared against other deep learning models and on two datasets, of which one was collected in a tank environment and the other was collected in a real marine environment. To further test the validity of the model, a real forward-looking sonar system was devised and employed in the lake trials. The results show that the proposed model performs better than the other models for small-object and few-sample classes and that it is competitive in semantic segmentation of forward-looking sonar images.
WOS关键词FUSION ; FIELD
资助项目National Science Foundation of China[62001418] ; National Science Foundation of China[62036009] ; Zhejiang Provincial Natural Science Foundation of China[LQ21F010011] ; NationalScience Foundation of China[U1909203] ; NationalScience Foundation of China[62005245]
WOS研究方向Chemistry ; Engineering ; Instruments & Instrumentation
语种英语
出版者MDPI
WOS记录号WOS:000883618000001
资助机构National Science Foundation of China ; Zhejiang Provincial Natural Science Foundation of China ; NationalScience Foundation of China
源URL[http://ir.idsse.ac.cn/handle/183446/9945]  
专题深海工程技术部_深海信息技术研究室
通讯作者Chen, Peng
作者单位1.Chinese Acad Sci, Inst Deep Sea Sci & Engn, Sanya 572000, Peoples R China
2.Zhejiang Univ Technol, Coll Informat Engn, Hangzhou 310023, Peoples R China
3.Zhejiang Univ Technol, Coll Comp Sci & Technol, Hangzhou 310023, Peoples R China
推荐引用方式
GB/T 7714		 Zhao, Dongdong,Ge, Weihao,Chen, Peng,et al. Feature Pyramid U-Net with Attention for Semantic Segmentation of Forward-Looking Sonar Images[J]. SENSORS,2022,22(21):22.
APA Zhao, Dongdong.,Ge, Weihao.,Chen, Peng.,Hu, Yingtian.,Dang, Yuanjie.,...&Guo, Xinxin.(2022).Feature Pyramid U-Net with Attention for Semantic Segmentation of Forward-Looking Sonar Images.SENSORS,22(21),22.
MLA Zhao, Dongdong,et al."Feature Pyramid U-Net with Attention for Semantic Segmentation of Forward-Looking Sonar Images".SENSORS 22.21(2022):22.

入库方式： OAI收割

来源：深海科学与工程研究所

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