基于汽车雷达的深度开放空间分割

F. Nowruzi, Dhanvin Kolhatkar, Prince Kapoor, Fahed Al Hassanat, E. J. Heravi, R. Laganière, Julien Rebut, Waqas Malik
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引用次数: 20

摘要

在这项工作中,我们提出使用雷达与先进的深度分割模型来识别停车场景中的开放空间。收集了一个名为SCORP的公开雷达观测数据集。用各种雷达输入表示对深度模型进行评估。我们提出的方法实现了低内存使用和实时处理速度,因此非常适合嵌入式部署。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Open Space Segmentation using Automotive Radar
In this work, we propose the use of radar with advanced deep segmentation models to identify open space in parking scenarios. A publically available dataset of radar observations called SCORP was collected. Deep models are evaluated with various radar input representations. Our proposed approach achieves low memory usage and real-time processing speeds, and is thus very well suited for embedded deployment.
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