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当前位置：工程学研究 > 2025年 4卷 (1)期

Open Access Article

Journal of Engineering Research. 2025; 4: (1) ; 46-58 ; DOI: 10.12208/j.jer.20250007.

Point-voxel 3D object detection method based on transfer block Transformer and feature pyramid
基于转移分块Transformer和特征金字塔的点-体素三维目标检测方法

作者： 刘良杰¹, 孙凯², 任宏乾³, 谢国涛⁴ *

¹株洲中车时代软件技术有限公司湖南株洲

²国家能源集团陕西神延煤炭有限责任公司西湾露天煤矿陕西榆林

³湖南大学机械与运载工程学院湖南长沙

⁴湖南大学无锡智能控制研究院江苏无锡

*通讯作者：谢国涛,单位：湖南大学无锡智能控制研究院江苏无锡；

国家自然科学基金项目（青年科学基金项目，项目批准号：52102456）

发布时间: 2025-01-20 总浏览量: 44

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摘要

随着环境感知技术的发展，激光雷达三维目标检测取得了显著进展。然而，基于体素的三维检测器在划分点云时，难以捕捉丰富的上下文信息和细节特征，尤其在处理遮挡和截断问题时，原始点云的细节信息常常丢失。为解决这些挑战，本文提出了一种新型数据增强策略，增强了模型对不完整点云的处理能力；并提出了基于转移分块Transformer和特征金字塔的点-体素三维目标检测模型PV-FMRTNet，有效解决了点云转换为体素过程中位置信息丢失的问题。此外，设计了一种新的二维特征编码网络，提升了基于体素的三维目标检测系统的性能。评估结果显示，本文模型在检测汽车、行人和骑行者方面的准确度分别达到84.30%、61.76%和78.08%，相比主流算法PointPillars等基准模型平均提升2.08%，展现出先进的准确性和鲁棒性。

关键词： 自动驾驶；深度学习；三维目标检测；特征金字塔；点体素

Abstract

With the development of environmental sensing technology, lidar three-dimensional target detection has made significant progress. However, it is difficult for voxel-based 3D detectors to capture rich contextual information and detailed features when dividing point clouds. Especially when dealing with occlusion and truncation problems, the detailed information of the original point cloud is often lost. To address these challenges, this paper proposes a new data augmentation strategy to enhance the model's ability to handle incomplete point clouds; and proposes a point-to-voxel 3D target detection model PV-FMRTNet based on transfer block Transformer and feature pyramid, which effectively solves the problem of position information loss in the process of converting point clouds to voxels. In addition, a new 2D feature encoding network was designed to improve the performance of the voxel-based 3D object detection system. The evaluation results show that the accuracy of the proposed model in detecting cars, pedestrians and cyclists reached 84.30%, 61.76% and 78.08% respectively, which is an average improvement of 2.08% over the mainstream algorithm PointPillars and other benchmark models, showing advanced accuracy and robustness.

Key words： Autonomous driving; Deep learning; 3D object detection; Feature pyramid; Point voxel

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引用本文

刘良杰, 孙凯, 任宏乾, 谢国涛, 基于转移分块Transformer和特征金字塔的点-体素三维目标检测方法[J]. 工程学研究, 2025; 4: (1) : 46-58.

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