Multibranch semantic image segmentation model based on edge optimization and category perception.
In semantic image segmentation tasks, most methods fail to fully use the characteristics of different scales and levels but rather directly perform upsampling. This may cause some effective information to be mistaken for redundant information and discarded, which in turn causes object segmentation c...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2024-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0315621 |
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| _version_ | 1841555456745537536 |
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| author | Zhuolin Yang Zhen Cao Jianfang Cao Zhiqiang Chen Cunhe Peng |
| author_facet | Zhuolin Yang Zhen Cao Jianfang Cao Zhiqiang Chen Cunhe Peng |
| author_sort | Zhuolin Yang |
| collection | DOAJ |
| description | In semantic image segmentation tasks, most methods fail to fully use the characteristics of different scales and levels but rather directly perform upsampling. This may cause some effective information to be mistaken for redundant information and discarded, which in turn causes object segmentation confusion. As a convolutional layer deepens, the loss of spatial detail information makes the segmentation effect achieved at the object boundary insufficiently accurate. To address the above problems, we propose an edge optimization and category-aware multibranch semantic segmentation network (ECMNet). First, an attention-guided multibranch fusion backbone network is used to connect features with different resolutions in parallel and perform multiscale information interaction to reduce the loss of spatial detail information. Second, a category perception module is used to learn category feature representations and guide the pixel classification process through an attention mechanism to optimize the resulting segmentation accuracy. Finally, an edge optimization module is used to integrate the edge features into the middle and the deep supervision layers of the network through an adaptive algorithm to enhance its ability to express edge features and optimize the edge segmentation effect. The experimental results show that the MIoU value reaches 79.2% on the Cityspaces dataset and 79.6% on the CamVid dataset, that the number of parameters is significantly lower than those of other models, and that the proposed method can effectively achieve improved semantic image segmentation performance and solve the partial category segmentation confusion problem, giving it certain application prospects. |
| format | Article |
| id | doaj-art-580b78db7cdd49dfb6f9af0f34dcfcc9 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-580b78db7cdd49dfb6f9af0f34dcfcc92025-01-08T05:32:51ZengPublic Library of Science (PLoS)PLoS ONE1932-62032024-01-011912e031562110.1371/journal.pone.0315621Multibranch semantic image segmentation model based on edge optimization and category perception.Zhuolin YangZhen CaoJianfang CaoZhiqiang ChenCunhe PengIn semantic image segmentation tasks, most methods fail to fully use the characteristics of different scales and levels but rather directly perform upsampling. This may cause some effective information to be mistaken for redundant information and discarded, which in turn causes object segmentation confusion. As a convolutional layer deepens, the loss of spatial detail information makes the segmentation effect achieved at the object boundary insufficiently accurate. To address the above problems, we propose an edge optimization and category-aware multibranch semantic segmentation network (ECMNet). First, an attention-guided multibranch fusion backbone network is used to connect features with different resolutions in parallel and perform multiscale information interaction to reduce the loss of spatial detail information. Second, a category perception module is used to learn category feature representations and guide the pixel classification process through an attention mechanism to optimize the resulting segmentation accuracy. Finally, an edge optimization module is used to integrate the edge features into the middle and the deep supervision layers of the network through an adaptive algorithm to enhance its ability to express edge features and optimize the edge segmentation effect. The experimental results show that the MIoU value reaches 79.2% on the Cityspaces dataset and 79.6% on the CamVid dataset, that the number of parameters is significantly lower than those of other models, and that the proposed method can effectively achieve improved semantic image segmentation performance and solve the partial category segmentation confusion problem, giving it certain application prospects.https://doi.org/10.1371/journal.pone.0315621 |
| spellingShingle | Zhuolin Yang Zhen Cao Jianfang Cao Zhiqiang Chen Cunhe Peng Multibranch semantic image segmentation model based on edge optimization and category perception. PLoS ONE |
| title | Multibranch semantic image segmentation model based on edge optimization and category perception. |
| title_full | Multibranch semantic image segmentation model based on edge optimization and category perception. |
| title_fullStr | Multibranch semantic image segmentation model based on edge optimization and category perception. |
| title_full_unstemmed | Multibranch semantic image segmentation model based on edge optimization and category perception. |
| title_short | Multibranch semantic image segmentation model based on edge optimization and category perception. |
| title_sort | multibranch semantic image segmentation model based on edge optimization and category perception |
| url | https://doi.org/10.1371/journal.pone.0315621 |
| work_keys_str_mv | AT zhuolinyang multibranchsemanticimagesegmentationmodelbasedonedgeoptimizationandcategoryperception AT zhencao multibranchsemanticimagesegmentationmodelbasedonedgeoptimizationandcategoryperception AT jianfangcao multibranchsemanticimagesegmentationmodelbasedonedgeoptimizationandcategoryperception AT zhiqiangchen multibranchsemanticimagesegmentationmodelbasedonedgeoptimizationandcategoryperception AT cunhepeng multibranchsemanticimagesegmentationmodelbasedonedgeoptimizationandcategoryperception |