无人车目标识别主干网络技术特点对比分析
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Comparison and Analysis of Technical Characteristics of Backbone Network for Target Recognition of Unmanned Vehicle
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    摘要:

    目标识别是无人车自动驾驶视觉感知模块的核心技术之一。当前,目标识别主要依靠主干网络提取特征, 进而对目标进行分类与回归。通常情况下,无人车嵌入式计算平台的计算与存储能力有限,为了降低主干网络的算 力与存储量,提升无人车的计算速度与效率,对目标分类任务的主干网络进行综合比较分析。围绕卷积核、感受野、 池化层、全连接层、激活函数等,以cifar10 和cifar100 为实验数据,从理论分析与数据实践层面,对主干网络算子 的选择与网络搭建进行分析对比,总结、归纳特征提取主干网络搭建的主要思路与做法。结果表明,该分析结论对 目标分类主干网络在嵌入式无人车系统中的应用具有一定的理论指导与参考价值。

    Abstract:

    Target recognition is one of the core technologies of the visual perception module of unmanned vehicle automatic driving. At present, target recognition mainly relies on the backbone network to extract features, and then classify and regress the target. In general, the computing and storage capacity of embedded computing platform for unmanned vehicles is limited. In order to reduce the computing power and storage capacity of the backbone network and improve the computing speed and efficiency of unmanned vehicles, this paper makes a comprehensive comparison and analysis of the backbone network for target classification tasks. Focusing on convolution kernel, receptive field, pooling layer, fully connected layer and activation function, taking cifar10 and cifar100 as experimental data, the selection of backbone network operators and network construction are analyzed and compared from the theoretical analysis and data practice level, and the main ideas and practices of feature extraction backbone network construction are summarized and summarized. The results show that the analysis conclusion has a certain theoretical guidance and reference value for the application of the target classification backbone network in the embedded unmanned vehicle system.

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樊胜利.无人车目标识别主干网络技术特点对比分析[J].,2024,43(01).

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  • 收稿日期:2023-09-08
  • 最后修改日期:2023-10-15
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  • 在线发布日期: 2024-01-26
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