Unification of Road Scene Segmentation Strategies Using Multistream Data and Latent Space Attention
Road scene understanding, as a field of research, has attracted increasing attention in recent years. The development of road scene understanding capabilities that are applicable to real-world road scenarios has seen numerous complications. This has largely been due to the cost and complexity of ach...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/17/7355 |
| _version_ | 1797581881221316608 |
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| author | August J. Naudé Herman C. Myburgh |
| author_facet | August J. Naudé Herman C. Myburgh |
| author_sort | August J. Naudé |
| collection | DOAJ |
| description | Road scene understanding, as a field of research, has attracted increasing attention in recent years. The development of road scene understanding capabilities that are applicable to real-world road scenarios has seen numerous complications. This has largely been due to the cost and complexity of achieving human-level scene understanding, at which successful segmentation of road scene elements can be achieved with a mean intersection over union score close to 1.0. There is a need for more of a unified approach to road scene segmentation for use in self-driving systems. Previous works have demonstrated how deep learning methods can be combined to improve the segmentation and perception performance of road scene understanding systems. This paper proposes a novel segmentation system that uses fully connected networks, attention mechanisms, and multiple-input data stream fusion to improve segmentation performance. Results show comparable performance compared to previous works, with a mean intersection over union of 87.4% on the Cityscapes dataset. |
| first_indexed | 2024-03-10T23:13:54Z |
| format | Article |
| id | doaj.art-ed61f77aeb9546a6993c1ac02d848934 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T23:13:54Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ed61f77aeb9546a6993c1ac02d8489342023-11-19T08:48:44ZengMDPI AGSensors1424-82202023-08-012317735510.3390/s23177355Unification of Road Scene Segmentation Strategies Using Multistream Data and Latent Space AttentionAugust J. Naudé0Herman C. Myburgh1Department of Electrical, Electronic and Computer Engineering, University of Pretoria, Pretoria 0002, South AfricaDepartment of Electrical, Electronic and Computer Engineering, University of Pretoria, Pretoria 0002, South AfricaRoad scene understanding, as a field of research, has attracted increasing attention in recent years. The development of road scene understanding capabilities that are applicable to real-world road scenarios has seen numerous complications. This has largely been due to the cost and complexity of achieving human-level scene understanding, at which successful segmentation of road scene elements can be achieved with a mean intersection over union score close to 1.0. There is a need for more of a unified approach to road scene segmentation for use in self-driving systems. Previous works have demonstrated how deep learning methods can be combined to improve the segmentation and perception performance of road scene understanding systems. This paper proposes a novel segmentation system that uses fully connected networks, attention mechanisms, and multiple-input data stream fusion to improve segmentation performance. Results show comparable performance compared to previous works, with a mean intersection over union of 87.4% on the Cityscapes dataset.https://www.mdpi.com/1424-8220/23/17/7355scene segmentationself-drivingdual attention mechanismsroad scene understandingdata fusion |
| spellingShingle | August J. Naudé Herman C. Myburgh Unification of Road Scene Segmentation Strategies Using Multistream Data and Latent Space Attention Sensors scene segmentation self-driving dual attention mechanisms road scene understanding data fusion |
| title | Unification of Road Scene Segmentation Strategies Using Multistream Data and Latent Space Attention |
| title_full | Unification of Road Scene Segmentation Strategies Using Multistream Data and Latent Space Attention |
| title_fullStr | Unification of Road Scene Segmentation Strategies Using Multistream Data and Latent Space Attention |
| title_full_unstemmed | Unification of Road Scene Segmentation Strategies Using Multistream Data and Latent Space Attention |
| title_short | Unification of Road Scene Segmentation Strategies Using Multistream Data and Latent Space Attention |
| title_sort | unification of road scene segmentation strategies using multistream data and latent space attention |
| topic | scene segmentation self-driving dual attention mechanisms road scene understanding data fusion |
| url | https://www.mdpi.com/1424-8220/23/17/7355 |
| work_keys_str_mv | AT augustjnaude unificationofroadscenesegmentationstrategiesusingmultistreamdataandlatentspaceattention AT hermancmyburgh unificationofroadscenesegmentationstrategiesusingmultistreamdataandlatentspaceattention |