Article
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Preserved in Portico This version is not peer-reviewed
3D Object Detection Using Multiple Frame Proposal Features Fusion
Version 1
: Received: 8 November 2023 / Approved: 9 November 2023 / Online: 9 November 2023 (11:16:01 CET)
A peer-reviewed article of this Preprint also exists.
Huang, M.; Leung, H.; Hou, M. 3D Object Detection Using Multiple-Frame Proposal Features Fusion. Sensors 2023, 23, 9162. Huang, M.; Leung, H.; Hou, M. 3D Object Detection Using Multiple-Frame Proposal Features Fusion. Sensors 2023, 23, 9162.
Abstract
Object detection is important in many applications, such as autonomous driving. While 2D images are lack of depth information and are sensitive to environmental conditions, 3D point cloud can provide accurate depth information and a more descriptive environment. However, sparsity is always a challenge in single-frame point cloud object detection. This paper introduces a two-stage proposal-based feature fusion method for object detection using multiple frames. The proposed method, called proposal features fusion (PFF), utilizes a cosine-similarity approach to associate proposals from multiple frames and employs an attention weighted fusion module to merge features from these proposals. It allows for feature fusion specific to individual objects and offers lower computational complexity while achieving higher precision. The experimental results on the nuScenes dataset demonstrate the effectiveness of our approach, achieving a mAP of 46.7%, which is 1.3% higher than the state-of-the-art 3D object detection method.
Keywords
autonomous driving; 3D object detection; multiple frame point clouds; feature and data fusion
Subject
Engineering, Electrical and Electronic Engineering
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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