Semantic Segmentation of Unmanned Driving Scene Based on Spatial Channel Dual Attention
An important issue in the field of unmanned driving is how to run real-time high-precision semantic segmentation models on low-power mobile electronic devices. Existing semantic segmentation algorithms have too many parameters and huge memory usage , which makes it difficult to meet the problems...
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| Format: | Article |
| Language: | zho |
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Harbin University of Science and Technology Publications
2023-10-01
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| Series: | Journal of Harbin University of Science and Technology |
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| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2263 |
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| author | WANGXiaoyu LINPeng |
| author_facet | WANGXiaoyu LINPeng |
| author_sort | WANGXiaoyu |
| collection | DOAJ |
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An important issue in the field of unmanned driving is how to run real-time high-precision semantic segmentation models on low-power mobile electronic devices. Existing semantic segmentation algorithms have too many parameters and huge memory usage , which makes it difficult to meet the problems of real-world applications such as unmanned driving. However , among the many factors that affect the accuracy and speed of the semantic segmentation model , spatial information and contextual features are particularly important , and it is difficult to take into account both. In response to this problem , it is proposed to use the incomplete ResNet18 as the backbone network , design a bilateral semantic segmentation model , and add a channel space dual attention model to the two paths to obtain more contextual and spatial information. In addition , the attention optimization module that refines the context information and the fusion module that integrates the output of the two paths are also used. Take Cityscapes and CamVid as data sets. On Citycapes , mIoU reached 77. 3% ; on CamVid , mIoU reached 66. 5% . When the input image resolution is 1024 × 2048 , the segmentation speed is 37. 9 ms. |
| format | Article |
| id | doaj-art-ca115cbc4c294c128e4880f832843d34 |
| institution | DOAJ |
| issn | 1007-2683 |
| language | zho |
| publishDate | 2023-10-01 |
| publisher | Harbin University of Science and Technology Publications |
| record_format | Article |
| series | Journal of Harbin University of Science and Technology |
| spelling | doaj-art-ca115cbc4c294c128e4880f832843d342025-08-20T02:40:11ZzhoHarbin University of Science and Technology PublicationsJournal of Harbin University of Science and Technology1007-26832023-10-01280510310910.15938/j.jhust.2023.05.013Semantic Segmentation of Unmanned Driving Scene Based on Spatial Channel Dual AttentionWANGXiaoyu0LINPeng1HarbinUniversityofScienceandTechnology,ComputerScienceandTechnology,Harbin150080,ChinaHarbinUniversityofScienceandTechnology,ComputerScienceandTechnology,Harbin150080,China An important issue in the field of unmanned driving is how to run real-time high-precision semantic segmentation models on low-power mobile electronic devices. Existing semantic segmentation algorithms have too many parameters and huge memory usage , which makes it difficult to meet the problems of real-world applications such as unmanned driving. However , among the many factors that affect the accuracy and speed of the semantic segmentation model , spatial information and contextual features are particularly important , and it is difficult to take into account both. In response to this problem , it is proposed to use the incomplete ResNet18 as the backbone network , design a bilateral semantic segmentation model , and add a channel space dual attention model to the two paths to obtain more contextual and spatial information. In addition , the attention optimization module that refines the context information and the fusion module that integrates the output of the two paths are also used. Take Cityscapes and CamVid as data sets. On Citycapes , mIoU reached 77. 3% ; on CamVid , mIoU reached 66. 5% . When the input image resolution is 1024 × 2048 , the segmentation speed is 37. 9 ms.https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2263driverlesstechnologyreal-timesemanticsegmentationdeeplearningattentionmechanismdepthseparableconvolu-tion |
| spellingShingle | WANGXiaoyu LINPeng Semantic Segmentation of Unmanned Driving Scene Based on Spatial Channel Dual Attention Journal of Harbin University of Science and Technology driverlesstechnology real-timesemanticsegmentation deeplearning attentionmechanism depthseparableconvolu-tion |
| title | Semantic Segmentation of Unmanned Driving Scene Based on Spatial Channel Dual Attention |
| title_full | Semantic Segmentation of Unmanned Driving Scene Based on Spatial Channel Dual Attention |
| title_fullStr | Semantic Segmentation of Unmanned Driving Scene Based on Spatial Channel Dual Attention |
| title_full_unstemmed | Semantic Segmentation of Unmanned Driving Scene Based on Spatial Channel Dual Attention |
| title_short | Semantic Segmentation of Unmanned Driving Scene Based on Spatial Channel Dual Attention |
| title_sort | semantic segmentation of unmanned driving scene based on spatial channel dual attention |
| topic | driverlesstechnology real-timesemanticsegmentation deeplearning attentionmechanism depthseparableconvolu-tion |
| url | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2263 |
| work_keys_str_mv | AT wangxiaoyu semanticsegmentationofunmanneddrivingscenebasedonspatialchanneldualattention AT linpeng semanticsegmentationofunmanneddrivingscenebasedonspatialchanneldualattention |