An adaptive coarse-fine semantic segmentation method for the attachment recognition on marine current turbines. (July 2021)

• Record Type: Journal Article
• Authors: Peng, Haiyang; Yang, Dingding; Wang, Tianzhen; Pandey, Shreya; Chen, Lisu; Shi, Ming; Diallo, Demba
• Is Part Of: Computers & electrical engineering. Volume 93(2021)
• Publication Date: 2021-07
• Subjects: Marine current turbine -- Data enlargement -- Semantic segmentation -- Adaptive attachment recognition -- Recognition uncertainty; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107182
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: A rotation augmentation strategy is utilized to generate sufficient labeled data without laborious manual labeling. A coarse-fine semantic segmentation network (CSSN) is proposed to recognize the attachment from blurry underwater images. An adaptive method is proposed to train the CSSN. Precise attachment area percentage and recognition uncertainty are inferred. Abstract: Microorganisms attached to marine current turbine may induce imbalance faults that badly affect the power generation efficiency. Therefore, it is necessary to conduct attachment recognition for prompt device maintenance. This paper proposes a coarse-fine semantic segmentation network (CSSN) to adaptively recognize the attachment location and size from blurry underwater images. The CSSN contains a deep coarse branch to perform global segmentation and a shallow fine branch to obtain local contours. The two branches are adaptively fused with dynamic weights in the training process. The final segmentation maps are produced by a softmax layer, after which the precise attachment area percentage can be computed. Besides, dropout is applied to estimate the recognition uncertainty that provides intuitive guidance for the maintenance decision. Experimental results show that the proposed method is efficient to recognize the attachment under turbid submerged conditions. Graphical abstract: Image, graphical abstract