Att-BEVFusion: An Object Detection Algorithm for Camera and LiDAR Fusion Under BEV Features Article Swipe
Peicheng Shi
,
Mengru Zhou
,
Xinlong Dong
,
Aixi Yang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/wevj15110539
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/wevj15110539
To improve the accuracy of detecting small and long-distance objects while self-driving cars are in motion, in this paper, we propose a 3D object detection method, Att-BEVFusion, which fuses camera and LiDAR data in a bird’s-eye view (BEV). First, the transformation from the camera view to the BEV space is achieved through an implicit supervision-based method, and then the LiDAR BEV feature point cloud is voxelized and converted into BEV features. Then, a channel attention mechanism is introduced to design a BEV feature fusion network to realize the fusion of camera BEV feature space and LiDAR BEV feature space. Finally, regarding the issue of insufficient global reasoning in the BEV fusion features generated by the channel attention mechanism, as well as the challenge of inadequate interaction between features. We further develop a BEV self-attention mechanism to apply global operations on the features. This paper evaluates the effectiveness of the Att-BEVFusion fusion algorithm on the nuScenes dataset, and the results demonstrate that the algorithm achieved 72.0% mean average precision (mAP) and 74.3% nuScenes detection score (NDS), with an advanced detection accuracy of 88.9% and 91.8% for single-item detection of automotive and pedestrian categories, respectively.
Related Topics
Concepts
Artificial intelligence
Computer science
Lidar
Computer vision
Feature (linguistics)
Object detection
Channel (broadcasting)
Pedestrian detection
Object (grammar)
Fusion mechanism
Feature vector
Fusion
Point cloud
Pattern recognition (psychology)
Remote sensing
Pedestrian
Geography
Philosophy
Linguistics
Lipid bilayer fusion
Computer network
Archaeology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/wevj15110539
- OA Status
- gold
- Cited By
- 1
- References
- 43
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4404542565
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4404542565Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/wevj15110539Digital Object Identifier
- Title
-
Att-BEVFusion: An Object Detection Algorithm for Camera and LiDAR Fusion Under BEV FeaturesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-11-20Full publication date if available
- Authors
-
Peicheng Shi, Mengru Zhou, Xinlong Dong, Aixi YangList of authors in order
- Landing page
-
https://doi.org/10.3390/wevj15110539Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.3390/wevj15110539Direct OA link when available
- Concepts
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Artificial intelligence, Computer science, Lidar, Computer vision, Feature (linguistics), Object detection, Channel (broadcasting), Pedestrian detection, Object (grammar), Fusion mechanism, Feature vector, Fusion, Point cloud, Pattern recognition (psychology), Remote sensing, Pedestrian, Geography, Philosophy, Linguistics, Lipid bilayer fusion, Computer network, ArchaeologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
- References (count)
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43Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.91.8% | 183 |
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| abstract_inverted_index.(NDS), | 174 |
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