Instance Segmentation Algorithm of Urban Street Scene Based on Data Augmentation and Feature Enhancement
Urban street scene segmentation is a key technology in the field of intelligent transportation. For the objective factors in the urban street scene environment such as occlusion, small objects, etc. , a DF-SOLO(Data Augmentation and Feature Enhancement SOLO) instance segmentation algorithm of urb...
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Harbin University of Science and Technology Publications
2024-04-01
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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=2309 |
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| author | LI Chengyan CHE Zixuan ZHENG Qisen |
| author_facet | LI Chengyan CHE Zixuan ZHENG Qisen |
| author_sort | LI Chengyan |
| collection | DOAJ |
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Urban street scene segmentation is a key technology in the field of intelligent transportation. For the objective factors in the urban street scene environment such as occlusion, small objects, etc. , a DF-SOLO(Data Augmentation and Feature Enhancement SOLO) instance segmentation algorithm of urban street scene based on data augmentation and feature enhancement is proposed. Aiming at the occlusion problem, the urban street view image is enhanced by the asymmetric self-encoder-decoder architecture. Compared with the traditional method, the processed image is closer to the real source data distribution. Aiming at the problem of small target segmentation in urban street scenes, the idea of feature weighting and feature fusion is introduced. The feature weighting module can assign different weights according to the importance of the features in the feature processing process, so as to improve the utilization rate of important features; the feature fusion module Multi-scale feature fusion is performed from a finer-grained perspective to solve the scale-sensitive problem and improve the descriptiveness of semantic features. Experiments on the Cityscapes dataset show that the proposed instance segmentation algorithm can improve the mAP value by 2. 1% and 2% respectively compared with the single-stage SOLO algorithm and the two-stage Mask R-CNN algorithm while ensuring real-time performance. Improved segmentation of small objects and occluded objects. |
| format | Article |
| id | doaj-art-e63fbaff738c4f6389b0bdf0b4b2ac57 |
| institution | Kabale University |
| issn | 1007-2683 |
| language | zho |
| publishDate | 2024-04-01 |
| publisher | Harbin University of Science and Technology Publications |
| record_format | Article |
| series | Journal of Harbin University of Science and Technology |
| spelling | doaj-art-e63fbaff738c4f6389b0bdf0b4b2ac572025-08-20T03:49:54ZzhoHarbin University of Science and Technology PublicationsJournal of Harbin University of Science and Technology1007-26832024-04-012902253210.15938/j.jhust.2024.02.004Instance Segmentation Algorithm of Urban Street Scene Based on Data Augmentation and Feature EnhancementLI Chengyan0CHE Zixuan1ZHENG Qisen2School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080 , ChinaSchool of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080 , ChinaSchool of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080 , China Urban street scene segmentation is a key technology in the field of intelligent transportation. For the objective factors in the urban street scene environment such as occlusion, small objects, etc. , a DF-SOLO(Data Augmentation and Feature Enhancement SOLO) instance segmentation algorithm of urban street scene based on data augmentation and feature enhancement is proposed. Aiming at the occlusion problem, the urban street view image is enhanced by the asymmetric self-encoder-decoder architecture. Compared with the traditional method, the processed image is closer to the real source data distribution. Aiming at the problem of small target segmentation in urban street scenes, the idea of feature weighting and feature fusion is introduced. The feature weighting module can assign different weights according to the importance of the features in the feature processing process, so as to improve the utilization rate of important features; the feature fusion module Multi-scale feature fusion is performed from a finer-grained perspective to solve the scale-sensitive problem and improve the descriptiveness of semantic features. Experiments on the Cityscapes dataset show that the proposed instance segmentation algorithm can improve the mAP value by 2. 1% and 2% respectively compared with the single-stage SOLO algorithm and the two-stage Mask R-CNN algorithm while ensuring real-time performance. Improved segmentation of small objects and occluded objects.https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2309instance segmentationsolo algorithmfeature extractiondata augmentationurban street scene |
| spellingShingle | LI Chengyan CHE Zixuan ZHENG Qisen Instance Segmentation Algorithm of Urban Street Scene Based on Data Augmentation and Feature Enhancement Journal of Harbin University of Science and Technology instance segmentation solo algorithm feature extraction data augmentation urban street scene |
| title | Instance Segmentation Algorithm of Urban Street Scene Based on Data Augmentation and Feature Enhancement |
| title_full | Instance Segmentation Algorithm of Urban Street Scene Based on Data Augmentation and Feature Enhancement |
| title_fullStr | Instance Segmentation Algorithm of Urban Street Scene Based on Data Augmentation and Feature Enhancement |
| title_full_unstemmed | Instance Segmentation Algorithm of Urban Street Scene Based on Data Augmentation and Feature Enhancement |
| title_short | Instance Segmentation Algorithm of Urban Street Scene Based on Data Augmentation and Feature Enhancement |
| title_sort | instance segmentation algorithm of urban street scene based on data augmentation and feature enhancement |
| topic | instance segmentation solo algorithm feature extraction data augmentation urban street scene |
| url | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2309 |
| work_keys_str_mv | AT lichengyan instancesegmentationalgorithmofurbanstreetscenebasedondataaugmentationandfeatureenhancement AT chezixuan instancesegmentationalgorithmofurbanstreetscenebasedondataaugmentationandfeatureenhancement AT zhengqisen instancesegmentationalgorithmofurbanstreetscenebasedondataaugmentationandfeatureenhancement |