MAT-PointPillars: Enhanced PointPillars algorithm based on multi-scale attention mechanisms and transformer Article Swipe
Xinpeng Yao
,
Peiyuan Liu
,
Jingmei Zhou
,
Zijian Wang
,
Songhua Fan
,
Yuchen Wang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0325373
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0325373
Aiming at the problem that small and irregular detection targets such as cyclists have low detection accuracy and inaccurate recognition by existing 3D target detection algorithms, MAT-PointPillars (Multi-scale Attention and Transformer PointPillars), a 3D object detection algorithm, extends PointPillars with multi-scale vision Transformers and attention mechanisms. First, the algorithm employs pillar coding for semantic point cloud encoding and introduces an attention mechanism to refine the backbone’s upsampling process. Furthermore, the Transformer Encoder is introduced to improve the upsampling structure of the third stage of the backbone. On the KITTI dataset, our algorithm achieved 3D average detection accuracy (AP3D) of 81.15%, 62.02%, and 58.68% across three difficulty levels. Compared with the baseline model, the proposed algorithm improves AP3D by 2.44%, 1.19%, and 1.23% respectively. The real-time 3D object detection system is built based on ROS, and average running frames per second of the system is 22.63, which is higher than the sampling frequency of conventional LiDAR. By ensuring sufficient detection speed, the MAT-PointPillars algorithm can increase detection accuracy of cyclists in real-world scenarios.
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- Language
- en
- Landing Page
- https://doi.org/10.1371/journal.pone.0325373
- OA Status
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- References
- 28
- Related Works
- 10
- OpenAlex ID
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https://openalex.org/W4411725198Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1371/journal.pone.0325373Digital Object Identifier
- Title
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MAT-PointPillars: Enhanced PointPillars algorithm based on multi-scale attention mechanisms and transformerWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-06-27Full publication date if available
- Authors
-
Xinpeng Yao, Peiyuan Liu, Jingmei Zhou, Zijian Wang, Songhua Fan, Yuchen WangList of authors in order
- Landing page
-
https://doi.org/10.1371/journal.pone.0325373Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1371/journal.pone.0325373Direct OA link when available
- Concepts
-
Upsampling, Computer science, Algorithm, Encoder, Artificial intelligence, Object detection, Point cloud, Transformer, Lidar, Pattern recognition (psychology), Computer vision, Engineering, Voltage, Remote sensing, Geology, Image (mathematics), Operating system, Electrical engineeringTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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28Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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