SLPOD:点云目标检测的超类学习

IF 4.6 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Xiaokang Yang, Kai Zhang, Yangyue Feng, Beibei Su, Yiming Cai, Kaibo Zhang, Zhiheng Zhang
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引用次数: 0

摘要

在点云目标检测领域,分类任务强调提取共同特征以增强泛化,通常以牺牲个体特定特征为代价。在处理像KITTI这样复杂的数据集时,这种限制变得特别明显。传统模型难以充分捕获个体特定的特征,导致特征空间内样本分布分散,影响了对象边界盒的精度。为了解决这一挑战,我们引入了基于超类的点云目标检测算法SLPOD。SLPOD采用连体网络结构,对同一类别内的样本进行无监督聚类,增强对个体特征的提取,从而提高面对复杂数据集时的检测精度。此外,我们的方法集成了体素和点云特征融合、全局特征获取和基于点稀疏度的采样率动态调整等策略,进一步增强了网络提取特征的能力。实验结果表明,SLPOD在KITTI和Waymo数据集上的平均精度优于基线算法,在不同场景下表现出鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SLPOD: superclass learning on point cloud object detection

In the realm of point cloud object detection, classification tasks emphasize extracting common features to enhance generalization, often at the expense of individual-specific features. This limitation becomes particularly evident when handling intricate datasets like KITTI. Traditional models struggle to adequately capture individual-specific features, resulting in a scattered distribution of samples within the feature space and compromising the precision of object bounding boxes. To tackle this challenge, we introduce SLPOD, a Superclass-based point cloud object detection algorithm. Employing a siamese network structure, SLPOD conducts unsupervised clustering of samples within the same category to enhance the extraction of individual-specific features, thereby improving detection accuracy when confronted with complex datasets. Additionally, our approach integrates strategies such as voxel and point cloud feature fusion, global feature acquisition, and dynamic adjustment of sampling rates based on point sparsity, further enhancing the network’s capability to extract features. Experimental results demonstrate that SLPOD outperforms baseline algorithms in mean Average Precision on both KITTI and Waymo datasets, exhibiting robustness across diverse scenarios.

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来源期刊
Complex & Intelligent Systems
Complex & Intelligent Systems COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
9.60
自引率
10.30%
发文量
297
期刊介绍: Complex & Intelligent Systems aims to provide a forum for presenting and discussing novel approaches, tools and techniques meant for attaining a cross-fertilization between the broad fields of complex systems, computational simulation, and intelligent analytics and visualization. The transdisciplinary research that the journal focuses on will expand the boundaries of our understanding by investigating the principles and processes that underlie many of the most profound problems facing society today.
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