A Lightweight Underwater Fish Image Semantic Segmentation Model Based on U-Net
aut.relation.journal | IET Image Processing | |
aut.relation.pages | 13 | |
dc.contributor.author | Zhang, Zhenkai | |
dc.contributor.author | Li, Wanghua | |
dc.contributor.author | Seet, Boon-Chong | |
dc.date.accessioned | 2024-06-27T01:56:20Z | |
dc.date.available | 2024-06-27T01:56:20Z | |
dc.date.issued | 2024-06-25 | |
dc.description.abstract | Semantic segmentation of underwater fish images is vital for monitoring fish stocks, assessing marine resources, and sustaining fisheries. To tackle challenges such as low segmentation accuracy, inadequate real-time performance, and imprecise location segmentation in current methods, a novel lightweight U-Net model is proposed. The proposed model acquires more segmentation details by applying a multiple-input approach at the first four encoder levels. To achieve both lightweight and high accuracy, a multi-scale residual structure (MRS) module is proposed to reduce parameters and compensate for the accuracy loss caused by the reduction of channels. To improve segmentation accuracy, a multi-scale skip connection (MSC) structure is further proposed, and the convolution block attention mechanism (CBAM) is introduced at the end of each decoder level for weight adjustment. Experimental results demonstrate a notable reduction in model volume, parameters, and floating-point operations by 94.20%, 94.39%, and 51.52% respectively, compared to the original model. The proposed model achieves a high mean intersection over union (mIOU) of 94.44%, mean pixel accuracy (mPA) of 97.03%, and a frame rate of 43.62 frames per second (FPS). With its high precision and minimal parameters, the model strikes a balance between accuracy and speed, making it particularly suitable for underwater image segmentation. | |
dc.identifier.citation | IET Image Processing, ISSN: 1751-9659 (Print); 1751-9667 (Online), Wiley. doi: 10.1049/ipr2.13161 | |
dc.identifier.doi | 10.1049/ipr2.13161 | |
dc.identifier.issn | 1751-9659 | |
dc.identifier.issn | 1751-9667 | |
dc.identifier.uri | http://hdl.handle.net/10292/17705 | |
dc.publisher | Wiley | |
dc.relation.uri | https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/ipr2.13161 | |
dc.rights | © 2024 The Author(s). IET Image Processing published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. | |
dc.rights.accessrights | OpenAccess | |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
dc.subject | 0801 Artificial Intelligence and Image Processing | |
dc.subject | 0906 Electrical and Electronic Engineering | |
dc.subject | Artificial Intelligence & Image Processing | |
dc.subject | 4603 Computer vision and multimedia computation | |
dc.subject | 4607 Graphics, augmented reality and games | |
dc.title | A Lightweight Underwater Fish Image Semantic Segmentation Model Based on U-Net | |
dc.type | Journal Article | |
pubs.elements-id | 558524 |
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