移动机器人的跨域室内视觉位置识别通过使用样式增强的泛化通过样式增强
1Department of Computer and Control Engineering, Faculty of Electrical and Computer Engineering, Rzeszow University of Technology, Al. Powstańców Warszawy 12, 35-959 Rzeszow, Poland.
Sensors (Basel, Switzerland)
|July 14, 2023
概括
本研究介绍了一种新的算法,用于室内视觉识别,使用卷积神经网络和风格随机化. 该方法提高了多域场景分类性能,达到92.08%的准确性.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 机器人技术 机器人技术 机器人技术
背景情况:
- 视觉识别系统经常与域名转移,如摄像机模型或环境的变化作斗争.
- 在各种室内环境中提高场景分类的稳定性对于自主系统至关重要.
研究的目的:
- 开发一种算法,用于对室内环境进行强大的多域视觉识别.
- 为了提高场景分类性能,使用来自各种领域的合成和现实世界的数据.
- 通过风格随机化和转移学习,提高模型在未见域上的性能.
主要方法:
- 使用了一个卷积神经网络 (CNN) 架构.
- 实施风格随机化技术来弥合领域的差距.
- 采用转移学习方法,使用风格扩展用于多域场景分类.
- 创建并利用了一个包含各种室内场景,摄像机模型和条件的数据集.
主要成果:
- 拟议的方法在多域场景分类中实现了92.08%的平均准确性.
- 多域数据和风格增强显著改善了模型性能.
- 该方法与以前报告的方法相比,显示出更优异的结果.
结论:
- 开发的算法有效地解决了室内视觉识别领域变化的挑战.
- 风格随机化和多域数据是提高场景分类模型概括能力的关键.
- 这些发现对改善人形机器人在复杂的室内环境中的性能有影响.
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