ACNet: An Attention–Convolution Collaborative Semantic Segmentation Network on Sensor-Derived Datasets for Autonomous Driving Article Swipe
Qiliang Zhang
,
Kaiwen Hua
,
Zhao Zhang
,
Yiwei Zhao
,
Pengpeng Chen
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/s25154776
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/s25154776
In intelligent vehicular networks, the accuracy of semantic segmentation in road scenes is crucial for vehicle-mounted artificial intelligence to achieve environmental perception, decision support, and safety control. Although deep learning methods have made significant progress, two main challenges remain: first, the difficulty in balancing global and local features leads to blurred object boundaries and misclassification; second, conventional convolutions have limited ability to perceive irregular objects, causing information loss and affecting segmentation accuracy. To address these issues, this paper proposes a global–local collaborative attention module and a spider web convolution module. The former enhances feature representation through bidirectional feature interaction and dynamic weight allocation, reducing false positives and missed detections. The latter introduces an asymmetric sampling topology and six-directional receptive field paths to effectively improve the recognition of irregular objects. Experiments on the Cityscapes, CamVid, and BDD100K datasets, collected using vehicle-mounted cameras, demonstrate that the proposed method performs excellently across multiple evaluation metrics, including mIoU, mRecall, mPrecision, and mAccuracy. Comparative experiments with classical segmentation networks, attention mechanisms, and convolution modules validate the effectiveness of the proposed approach. The proposed method demonstrates outstanding performance in sensor-based semantic segmentation tasks and is well-suited for environmental perception systems in autonomous driving.
Related Topics
Concepts
Computer science
Segmentation
Convolution (computer science)
Artificial intelligence
Feature (linguistics)
Computer vision
Representation (politics)
Field (mathematics)
Pattern recognition (psychology)
Object detection
Feature extraction
Artificial neural network
Law
Linguistics
Political science
Politics
Pure mathematics
Mathematics
Philosophy
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s25154776
- https://www.mdpi.com/1424-8220/25/15/4776/pdf?version=1754206976
- OA Status
- gold
- References
- 48
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4412931304
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4412931304Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/s25154776Digital Object Identifier
- Title
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ACNet: An Attention–Convolution Collaborative Semantic Segmentation Network on Sensor-Derived Datasets for Autonomous DrivingWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2025Year of publication
- Publication date
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2025-08-03Full publication date if available
- Authors
-
Qiliang Zhang, Kaiwen Hua, Zhao Zhang, Yiwei Zhao, Pengpeng ChenList of authors in order
- Landing page
-
https://doi.org/10.3390/s25154776Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8220/25/15/4776/pdf?version=1754206976Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/1424-8220/25/15/4776/pdf?version=1754206976Direct OA link when available
- Concepts
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Computer science, Segmentation, Convolution (computer science), Artificial intelligence, Feature (linguistics), Computer vision, Representation (politics), Field (mathematics), Pattern recognition (psychology), Object detection, Feature extraction, Artificial neural network, Law, Linguistics, Political science, Politics, Pure mathematics, Mathematics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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48Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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