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简单的可扩展多模式语义细分模型

Yuchang Zhu1, Nanfeng Xiao1

  • 1School of Computer Science & Engineering, South China University of Technology, Guangzhou 510006, China.

Sensors (Basel, Switzerland)
|January 26, 2024
PubMed
概括
此摘要是机器生成的。

将更多的传感器添加到自动驾驶系统中并不总是改善视觉感知. 我们的新型多式联机模型可以保持或提高任何传感器的精度,性能优于现有方法.

关键词:
自动驾驶自动驾驶的自动驾驶.多式联络多式联络多模式的语义细分,多模式的语义细分.语义细分 语义细分 语义细分 语义细分视觉感知 视觉感知 视觉感知

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科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 机器人技术 机器人技术 机器人技术

背景情况:

  • 视觉感知对于自动驾驶至关重要.
  • 传统方法使用单模RGB图像进行语义细分.
  • 多模式方法提供补充数据,以提高可靠性.

研究的目的:

  • 开发一种用于自动驾驶的多式视觉感知模型.
  • 为了解决在传统模型中添加模式时观察到的准确性下降.
  • 创建一个可扩展的模型,以任何方式保持或提高准确性.

主要方法:

  • 提出了一种多式模式,其中有一个RGB主分支,并为其他模式提供共享的骨干.
  • 引入了Modals分数模块 (MSM) 来评估特征的重要性.
  • 集成了功能补充模块 (FCM) 进行增强的功能融合.
  • 应用剩余思维来改善所有行业的特征提取.

主要成果:

  • 将模式添加到传统模型中可以降低语义细分的准确性.
  • 拟议的多式联运模式保持或提高准确性与任何额外的模式.
  • 该模型超越了当前最先进的多模式语义细分方法.

结论:

  • 拟议的多式联运模式对于自动驾驶的视觉感知是有效和可扩展的.
  • MSM,FCM和剩余思维对模型的性能做出了重大贡献.
  • 这种方法提供了一个强大的解决方案,用于增强自主系统中的语义细分.