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记忆增强的3D点云语义细分网络用于智能采矿

Yunhao Cui¹, Zhihui Zhang¹, Yi An²

¹School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang 471023, China.

Sensors (Basel, Switzerland)

|July 13, 2024

概括

本研究介绍了一种新的3D点云语义细分网络,用于智能采矿. 改进后的模型通过解决数据不平衡和特征提取挑战来提高自主操作的准确性和安全性.

科学领域:

机器人和自动化机器人和自动化
计算机视觉计算机视觉
人工智能的人工智能

背景情况:

精确的3D语义细分对于智能采矿的自主操作至关重要.
挑战包括复杂的环境,多样化的目标和不平衡的样本数据,导致细分精度低.
这影响了自动挖掘和装载操作的可靠性和安全性.

研究的目的:

提出一个3D点云语义细分网络,以增强自主采矿操作.
为了应对样本分布不均和特征提取不足的挑战.
为了提高模型的轻量化性质和部署能力.

主要方法:

开发了一种记忆增强学习机制,用于关键语义特征的记忆模块.
实施了道注意力机制,以改进特征表达和权重.
使用深度可分离卷积来实现轻量级模型架构,减少参数数量.

主要成果:

拟议的网络显著提高了3D语义细分的准确性.
与控制方法相比,平均准确度提高了7.15%.
证明了关键特征的增强提取和不平衡数据集的改进处理.

结论:

这种新型网络有效地提高了智能采矿的3D语义细分精度.
记忆增强和注意力机制成功地解决了数据不平衡和特征表示问题.
轻量级设计提高了模型的部署性,有助于实现更安全,更准确的自主采矿操作.

关键词:

3D点云语义细分 3D点云语义细分智能采矿子智能采矿轻量级的注意力机制.增强记忆增强记忆增强记忆

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