Bibliographic Details
| Title: |
MSMFAM-VoxelNeXt: LiDAR-Camera Fusion for Highway Traffic Perception. |
| Authors: |
ZHANG, Chunsheng1 77862936@qq.com, LIU, Bibo2, WANG, Changwei2 |
| Source: |
Technical Gazette / Tehnički Vjesnik. Oct2025, Vol. 32 Issue 5, p1714-1722. 9p. |
| Subjects: |
LIDAR, Multisensor data fusion, Traffic monitoring, Electronic data processing, Intelligent transportation systems |
| Abstract: |
Roadside perception is critical for intelligent transportation systems, but faces challenges in sensor fusion and data processing. This paper proposes an enhanced perception scheme integrating LiDAR and camera data. We introduce a multi-scale multi-feature attention module (MSMFAM) to enrich voxel features, addressing issues of voxel size and semantic information extraction. Point cloud levelling and data simulation augmentation techniques improve detection accuracy across varying sensor heights. Our fusion algorithm combines LiDAR and image results with elliptical matching for enhanced target detection and classification. Experimental results show significant improvements over baseline algorithms, with mAP increases of 2.2% in point cloud detection and 1.5% infusion results. The proposed method demonstrates potential for advancing roadside perception in intelligent transportation systems. [ABSTRACT FROM AUTHOR] |
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| Database: |
Engineering Source |
