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A MULTI-HEAD ATTENTION APPROACH WITH COMPLEMENTARY MULTIMODAL FUSION FOR VEHICLE DETECTION

<p dir="ltr">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) 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.</p>

  1. 10.25394/pgs.25270039.v1
Identiferoai:union.ndltd.org:purdue.edu/oai:figshare.com:article/25270039
Date03 June 2024
CreatorsNujhat Tabassum (18010969)
Source SetsPurdue University
Detected LanguageEnglish
TypeText, Thesis
RightsCC BY 4.0
Relationhttps://figshare.com/articles/thesis/A_MULTI-HEAD_ATTENTION_APPROACH_WITH_COMPLEMENTARY_MULTIMODAL_FUSION_FOR_VEHICLE_DETECTION/25270039

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