3D-DFM: anchor-free multimodal 3-D object detection with dynamic fusion module for autonomous driving

Lin, C., Tian, D., Duan, X., Zhou, J., Zhao, D. and Cao, D. (2023) 3D-DFM: anchor-free multimodal 3-D object detection with dynamic fusion module for autonomous driving. IEEE Transactions on Neural Networks and Learning Systems, 34(12), pp. 10812-10822. (doi: 10.1109/TNNLS.2022.3171553) (PMID:35560081)

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Abstract

Recent advances in cross-modal 3D object detection rely heavily on anchor-based methods, and however, intractable anchor parameter tuning and computationally expensive postprocessing severely impede an embedded system application, such as autonomous driving. In this work, we develop an anchor-free architecture for efficient camera-light detection and ranging (LiDAR) 3D object detection. To highlight the effect of foreground information from different modalities, we propose a dynamic fusion module (DFM) to adaptively interact images with point features via learnable filters. In addition, the 3D distance intersection-over-union (3D-DIoU) loss is explicitly formulated as a supervision signal for 3D-oriented box regression and optimization. We integrate these components into an end-to-end multimodal 3D detector termed 3D-DFM. Comprehensive experimental results on the widely used KITTI dataset demonstrate the superiority and universality of 3D-DFM architecture, with competitive detection accuracy and real-time inference speed. To the best of our knowledge, this is the first work that incorporates an anchor-free pipeline with multimodal 3D object detection.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Zhao, Dr Dezong
Authors: Lin, C., Tian, D., Duan, X., Zhou, J., Zhao, D., and Cao, D.
College/School:College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
Journal Name:IEEE Transactions on Neural Networks and Learning Systems
Publisher:IEEE
ISSN:2162-237X
ISSN (Online):2162-2388
Published Online:13 May 2022
Copyright Holders:Copyright © 2022 IEEE
First Published:First published in IEEE Transactions on Neural Networks and Learning Systems 34(12):10812 - 10822
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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