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MSMFAM-VoxelNeXt: LiDAR-Camera Fusion for Highway Traffic Perception.

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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]
Copyright of Technical Gazette / Tehnički Vjesnik is the property of Tehnicki Vjesnik and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: MSMFAM-VoxelNeXt: LiDAR-Camera Fusion for Highway Traffic Perception.
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  Data: <searchLink fieldCode="AR" term="%22ZHANG%2C+Chunsheng%22">ZHANG, Chunsheng</searchLink><relatesTo>1</relatesTo><i> 77862936@qq.com</i><br /><searchLink fieldCode="AR" term="%22LIU%2C+Bibo%22">LIU, Bibo</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22WANG%2C+Changwei%22">WANG, Changwei</searchLink><relatesTo>2</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Technical+Gazette+%2F+Tehnički+Vjesnik%22">Technical Gazette / Tehnički Vjesnik</searchLink>. Oct2025, Vol. 32 Issue 5, p1714-1722. 9p.
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  Data: <searchLink fieldCode="DE" term="%22LIDAR%22">LIDAR</searchLink><br /><searchLink fieldCode="DE" term="%22Multisensor+data+fusion%22">Multisensor data fusion</searchLink><br /><searchLink fieldCode="DE" term="%22Traffic+monitoring%22">Traffic monitoring</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+data+processing%22">Electronic data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Intelligent+transportation+systems%22">Intelligent transportation systems</searchLink>
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  Data: 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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  Data: <i>Copyright of Technical Gazette / Tehnički Vjesnik is the property of Tehnicki Vjesnik and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.17559/TV-20240730001888
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      – SubjectFull: Traffic monitoring
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              Text: Oct2025
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              Y: 2025
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