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Vehicle Detection in Adverse Weather: A Multi-Head Attention Approach with Multimodal Fusion.
- Source :
- Journal of Low Power Electronics & Applications; Jun2024, Vol. 14 Issue 2, p23, 15p
- Publication Year :
- 2024
-
Abstract
- In the realm of autonomous vehicle technology, the multimodal vehicle detection network (MVDNet) represents a significant leap forward, particularly in the challenging context of weather conditions. This paper focuses on the enhancement of MVDNet through the integration of a multi-head attention layer, aimed at refining its performance. The integrated multi-head attention layer in the MVDNet model is a pivotal modification, advancing the network's ability to process and fuse multimodal sensor information more efficiently. The paper validates the improved performance of MVDNet with multi-head attention through comprehensive testing, which includes a training dataset derived from the Oxford Radar RobotCar. The results clearly demonstrate that the multi-head MVDNet outperforms the other related conventional models, particularly in the average precision (AP) of estimation, under challenging environmental conditions. The proposed multi-head MVDNet not only contributes significantly to the field of autonomous vehicle detection but also underscores the potential of sophisticated sensor fusion techniques in overcoming environmental limitations. [ABSTRACT FROM AUTHOR]
- Subjects :
- TRANSFORMER models
OBJECT recognition (Computer vision)
WEATHER
AUTONOMOUS vehicles
Subjects
Details
- Language :
- English
- ISSN :
- 20799268
- Volume :
- 14
- Issue :
- 2
- Database :
- Complementary Index
- Journal :
- Journal of Low Power Electronics & Applications
- Publication Type :
- Academic Journal
- Accession number :
- 178195020
- Full Text :
- https://doi.org/10.3390/jlpea14020023