MLHI-Net: multi-level hybrid lightweight water body segmentation network for urban shoreline detection
Abstract The capacity to detect shorelines is critical for the autonomous navigation of Unmanned Surface Vehicles (USVs). The majority of extant methods are unable to adapt to the discrimination of high similarity features between the shore and reflections in complex and diverse environments. Moreov...
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-87209-y |
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| author | Jianhua Ye Pan Li Yunda Zhang Ze Guo Shoujin Zeng Youji Zhan |
| author_facet | Jianhua Ye Pan Li Yunda Zhang Ze Guo Shoujin Zeng Youji Zhan |
| author_sort | Jianhua Ye |
| collection | DOAJ |
| description | Abstract The capacity to detect shorelines is critical for the autonomous navigation of Unmanned Surface Vehicles (USVs). The majority of extant methods are unable to adapt to the discrimination of high similarity features between the shore and reflections in complex and diverse environments. Moreover, they are also incapable of accurately extracting fuzzy boundaries caused by different scenes and climatic conditions. To address these challenges, this paper proposes a multi-level hybrid lightweight water segmentation network, MLHI-Net. First, we design a convolutional module (ORRD) compatible with over-parameterized and redundancy removal techniques based on lightweight design. The over-parameterized convolutional layers enhance the interactive ability of feature representation and context information. The removal of spatial and channel redundancy, in conjunction with interactive reconstruction, serves to simulate attention and enhance the learning ability of waterscape. Then, we design a multi-branch two-layer attention fusion module (MDA), which achieves diverse attention and global optimisation of edge details by connecting spatial attention, channel attention and pixel attention in parallel. thereby guiding feature fusion and solving the problem of receptive field mismatch. This module guides feature fusion and solves the problem of receptive field mismatch. To validate the proposed methodology, a dataset, CityWater, was constructed, with multiple fields and climatic conditions, and a substantial number of experiments were conducted on this and other public datasets. Experimental results show that MLHI-Net outperforms other advanced segmentation networks in Mean Intersection over Union (MIoU) and Pixel Accuracy (PA) on the CityWater and USVInland datasets, with MIoU of 97.86% and PA of 98.92% on the CityWater dataset, and MIoU of 98.12% and PA of 99.10% on the USVInland dataset. Additionally, the network’s computational GLOPS is 13.45 G, and the number of parameters is 46.92 M, which can meet the requirements for real-time detection. The MLHI-Net has been shown to perform well in a range of environments. In addition, it has good generalisation capabilities, providing reliable support to the autonomous system. |
| format | Article |
| id | doaj-art-bc669037b52b4efb99d7a83e481873b5 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-bc669037b52b4efb99d7a83e481873b52025-02-09T12:29:13ZengNature PortfolioScientific Reports2045-23222025-02-0115112010.1038/s41598-025-87209-yMLHI-Net: multi-level hybrid lightweight water body segmentation network for urban shoreline detectionJianhua Ye0Pan Li1Yunda Zhang2Ze Guo3Shoujin Zeng4Youji Zhan5School of Mechanical and Automotive Engineering, Fujian University of TechnologySchool of Mechanical and Automotive Engineering, Fujian University of TechnologySchool of Mechanical and Automotive Engineering, Fujian University of TechnologySchool of Mechanical and Automotive Engineering, Fujian University of TechnologySchool of Mechanical and Automotive Engineering, Fujian University of TechnologySchool of Mechanical and Automotive Engineering, Fujian University of TechnologyAbstract The capacity to detect shorelines is critical for the autonomous navigation of Unmanned Surface Vehicles (USVs). The majority of extant methods are unable to adapt to the discrimination of high similarity features between the shore and reflections in complex and diverse environments. Moreover, they are also incapable of accurately extracting fuzzy boundaries caused by different scenes and climatic conditions. To address these challenges, this paper proposes a multi-level hybrid lightweight water segmentation network, MLHI-Net. First, we design a convolutional module (ORRD) compatible with over-parameterized and redundancy removal techniques based on lightweight design. The over-parameterized convolutional layers enhance the interactive ability of feature representation and context information. The removal of spatial and channel redundancy, in conjunction with interactive reconstruction, serves to simulate attention and enhance the learning ability of waterscape. Then, we design a multi-branch two-layer attention fusion module (MDA), which achieves diverse attention and global optimisation of edge details by connecting spatial attention, channel attention and pixel attention in parallel. thereby guiding feature fusion and solving the problem of receptive field mismatch. This module guides feature fusion and solves the problem of receptive field mismatch. To validate the proposed methodology, a dataset, CityWater, was constructed, with multiple fields and climatic conditions, and a substantial number of experiments were conducted on this and other public datasets. Experimental results show that MLHI-Net outperforms other advanced segmentation networks in Mean Intersection over Union (MIoU) and Pixel Accuracy (PA) on the CityWater and USVInland datasets, with MIoU of 97.86% and PA of 98.92% on the CityWater dataset, and MIoU of 98.12% and PA of 99.10% on the USVInland dataset. Additionally, the network’s computational GLOPS is 13.45 G, and the number of parameters is 46.92 M, which can meet the requirements for real-time detection. The MLHI-Net has been shown to perform well in a range of environments. In addition, it has good generalisation capabilities, providing reliable support to the autonomous system.https://doi.org/10.1038/s41598-025-87209-yUnmanned surface vehiclesShoreline detectionWater segmentation networkORRDMDA |
| spellingShingle | Jianhua Ye Pan Li Yunda Zhang Ze Guo Shoujin Zeng Youji Zhan MLHI-Net: multi-level hybrid lightweight water body segmentation network for urban shoreline detection Scientific Reports Unmanned surface vehicles Shoreline detection Water segmentation network ORRD MDA |
| title | MLHI-Net: multi-level hybrid lightweight water body segmentation network for urban shoreline detection |
| title_full | MLHI-Net: multi-level hybrid lightweight water body segmentation network for urban shoreline detection |
| title_fullStr | MLHI-Net: multi-level hybrid lightweight water body segmentation network for urban shoreline detection |
| title_full_unstemmed | MLHI-Net: multi-level hybrid lightweight water body segmentation network for urban shoreline detection |
| title_short | MLHI-Net: multi-level hybrid lightweight water body segmentation network for urban shoreline detection |
| title_sort | mlhi net multi level hybrid lightweight water body segmentation network for urban shoreline detection |
| topic | Unmanned surface vehicles Shoreline detection Water segmentation network ORRD MDA |
| url | https://doi.org/10.1038/s41598-025-87209-y |
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