Communication Dans Un Congrès Année : 2025

MEFA-MS: Attention-Based U-Net for Pedestrian and Vehicle Detection

Résumé

A Multimodal image Early Fusion with Attention (MEFA) module can be employed for visual object detection, leveraging the robust capabilities of well-established monomodal computer vision models. However, it exhibits poor inference time under real-world usage conditions. The present paper introduces a novel module, referred to as MEFA-MS. This module proposes a U-Net encoder-decoder architecture incorporating spatial and channel attention mechanisms. It aims to reduce inference time while maintaining high precision for pedestrians and vehicles detection. We conduct a comparative study between the proposed MEFA-MS module and the predecessor MEFA module. The proposed approach entails a 2% improvement in mAP50, accompanied by a 50% reduction in inference time for pedestrian and vehicle detection on the DENSE dataset, employing the RT-DETR object detector. Moreover, we demonstrate that the system exhibits superior performance in all weather conditions, achieving a 4% reduction in false positives compared to the previous MEFA module.

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hal-05368360 , version 1 (17-11-2025)

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  • HAL Id : hal-05368360 , version 1

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Yoann Dupas, Olivier Hotel, Grégoire Lefebvre, Christophe Cérin. MEFA-MS: Attention-Based U-Net for Pedestrian and Vehicle Detection. ICMV 2025 - Eighteenth International Conference on Machine Vision, Oct 2025, Paris, France. pp.1-8. ⟨hal-05368360⟩
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