Lane Road Segmentation Based on Improved UNet Architecture for Autonomous Driving Article Swipe
Hoang Ngoc Tran
,
Huynh Vu Nhu Nguyen
,
Khang Hoang Nguyen
,
Luyl-Da Quach
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.14569/ijacsa.2023.0140724
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.14569/ijacsa.2023.0140724
This paper introduces a real-time workflow for implementing neural networks in the context of autonomous driving. The UNet architecture is specifically selected for road segmentation due to its strong performance and low complexity. To further improve the model's capabilities, Local Binary Convolution (LBC) is incorporated into the skip connections, enhancing feature extraction, and elevating the Intersection over Union (IoU) metric. The performance evaluation of the model focuses on road detection, utilizing the IOU metric. Two datasets are used for training and validation: the widely used KITTI dataset and a custom dataset collected within the ROS2 environment. Simulation validation is performed on both datasets to assess the performance of our model. The evaluation of our model on the KITTI dataset demonstrates an impressive IoU score of 97.90% for road segmentation. Moreover, when evaluated on our custom dataset, our model achieves an IoU score of 98.88%, which is comparable to the performance of conventional UNet models. Our proposed method to reconstruct the model structure and provide input feature extraction can effectively improve the performance of existing lane road segmentation methods.
Related Topics
Concepts
Computer science
Segmentation
Metric (unit)
Artificial intelligence
Context (archaeology)
Intersection (aeronautics)
Workflow
Feature extraction
Feature (linguistics)
Convolution (computer science)
Pattern recognition (psychology)
Data mining
Machine learning
Computer vision
Artificial neural network
Database
Aerospace engineering
Economics
Engineering
Biology
Operations management
Philosophy
Linguistics
Paleontology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.14569/ijacsa.2023.0140724
- http://thesai.org/Downloads/Volume14No7/Paper_24-Lane_Road_Segmentation_Based_on_Improved_UNet_Architecture.pdf
- OA Status
- diamond
- Cited By
- 11
- References
- 30
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385586875
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4385586875Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.14569/ijacsa.2023.0140724Digital Object Identifier
- Title
-
Lane Road Segmentation Based on Improved UNet Architecture for Autonomous DrivingWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-01-01Full publication date if available
- Authors
-
Hoang Ngoc Tran, Huynh Vu Nhu Nguyen, Khang Hoang Nguyen, Luyl-Da QuachList of authors in order
- Landing page
-
https://doi.org/10.14569/ijacsa.2023.0140724Publisher landing page
- PDF URL
-
https://thesai.org/Downloads/Volume14No7/Paper_24-Lane_Road_Segmentation_Based_on_Improved_UNet_Architecture.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://thesai.org/Downloads/Volume14No7/Paper_24-Lane_Road_Segmentation_Based_on_Improved_UNet_Architecture.pdfDirect OA link when available
- Concepts
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Computer science, Segmentation, Metric (unit), Artificial intelligence, Context (archaeology), Intersection (aeronautics), Workflow, Feature extraction, Feature (linguistics), Convolution (computer science), Pattern recognition (psychology), Data mining, Machine learning, Computer vision, Artificial neural network, Database, Aerospace engineering, Economics, Engineering, Biology, Operations management, Philosophy, Linguistics, PaleontologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
11Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 3, 2024: 8Per-year citation counts (last 5 years)
- References (count)
-
30Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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