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To address the issue that existing point cloud-based 3D object detection algorithms perform poorly on small object detection, a PointPillars 3D object detection algorithm based on an attention mechanism is proposed on the basis of the traditional PointPillars model. Firstly, the pillar feature network in the PointPillars model is improved by introducing a set of Efficient Channel Attention (ECA) mechanisms in the encoding network, allowing each Pillar module to better consider its local detailed geometric information, thereby significantly enhancing the feature representation capability of each Pillar module. Secondly, a Shuffle Attention module is introduced into the 2D convolutional downsampling module of the backbone network. By improving the diversity and effectiveness of feature representation, the model network focuses on important features while suppressing less important or redundant features, significantly enhancing the algorithm's feature extraction ability. Test results on the KITTI dataset show that the improved algorithm improves the 3D pedestrian detection accuracy by 4.04 percentage points, 3.43 percentage points and 1.93 percentage points for easy, moderate, and hard difficulties, respectively, significantly outperforming the original PointPillars network and enhancing the performance of small pedestrian object detection.
, authors=Yatao Yu
1, Hongrui Zhai
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为解决现有的基于点云的三维目标检测算法对小目标检测效果较差的问题,在传统点柱(PointPillars)模型的基础上提出了一种基于注意力机制的PointPillars三维目标检测算法。首先,对PointPillars模型中的支柱(Pillar)特征网络进行改进,在编码网络中引入一组高效通道注意力(ECA)机制,使每个Pillar模块能够更充分地考虑其局部详细几何信息,从而显著提高每个Pillar模块的特征表示能力;其次,在主干网络中的二维卷积下采样模块中引入混洗注意力(Shuffle Attention)模块,通过提升特征表达的多样性和有效性,使模型网络聚焦于重要特征并抑制不重要或冗余特征,从而显著增强了算法的特征提取能力。在KITTI数据集上的测试结果表明,改进算法在三维行人检测的简单、中等和困难3种难度下的检测精度分别提升了4.04百分点、3.43百分点和1.93百分点,显著优于原始的PointPillars网络,行人小目标检测性能得到提升。
, authors=于亚涛
1, 翟宏瑞
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