合成和现实世界的数据集用于在不同天气和照明条件下的行人交叉路口细分
Krešimir Romić1, Hrvoje Leventić1, Marija Habijan1
1Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Kneza Trpimira 2B, Osijek HR-31000, Croatia.
Data in brief
|June 30, 2025
概括
本研究介绍了视觉障碍者辅助技术的多样化的行人交叉路口数据集. 它包括真实和合成图像与细分面具来改进计算机视觉算法导航.
科学领域:
- 计算机视觉 计算机视觉
- 辅助技术 辅助技术 辅助技术
- 机器学习 机器学习
背景情况:
- 不同步行交叉路口数据集的有限可用性阻碍了对视力受损人群的辅助技术的开发.
- 现有的数据集往往缺乏各种环境条件和现实世界的视角.
研究的目的:
- 为了呈现一个新的,全面的数据集,用于行人交叉路口的细分.
- 支持计算机视觉算法用于辅助导航的开发和基准测试.
- 为了促进对合成数据增强的研究,用于现实世界的应用.
主要方法:
- 使用精心调整的稳定扩散模型生成3000张合成图像,具有各种环境提示.
- 收藏300个现实世界,第一人称视图图像从智能手机记录在不同的条件下.
- 对所有图像进行手动注释,以创建精确的行人交叉路口二进制细分面具.
主要成果:
- 一个数据集将3300张图像 (3000张合成图像,300张真实图像) 与相应的行人交叉路口分段面具相结合.
- 按图像源和环境条件 (阳光,阴云,雨,夜晚) 的等级组织.
- 通过各种提示,环境条件和物理位置的有限重复来确保多样性.
结论:
- 该数据集解决了公开可用的,多样化的行人交叉路口数据中的关键差距.
- 它是推动计算机视觉在辅助导航和行人安全方面的宝贵资源.
- 评估合成数据增强和改善视力障碍者AI的潜力.
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