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首页> 外文会议>IEEE International Conference on Multimedia and Expo >DMPR-PS: A Novel Approach for Parking-Slot Detection Using Directional Marking-Point Regression
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DMPR-PS: A Novel Approach for Parking-Slot Detection Using Directional Marking-Point Regression

机译：DMPR-PS：使用方向标记回归的一种新方法。使用方向标记回归停车槽检测

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The self-parking system plays an important role in autonomous driving, and one of its critical issues is parking-slot detection. Previous studies in this field are mostly based on off-the-shelf models designed for universal purposes, which have various limitations in solving specific problems. In this paper, we propose a parking-slot detection method using directional marking-point regression, namely DMPR-PS. Instead of utilizing multiple off-the-shelf models, DMPR-PS uses a novel CNN-based model specially designed for directional marking-point regression. Given a surround-view image I, the model predicts position, shape and orientation of each marking-point on I. From marking-points, parking-slots on I could be easily inferred using geometric rules. DMPR-PS outperforms state-of-the-art competitors on the benchmark dataset with a precision rate of 99.42% and a recall rate of 99.37%, while achieving a real-time detection speed of 12ms per frame on Nvidia Titan Xp. To make the results reproducible, the source code is available at https://github.com/Teoge/DMPR-PS.
机译：自停车系统在自动驾驶中起着重要作用，其关键问题之一是停放槽检测。在此领域的先前研究主要基于为普遍目的而设计的现成模型，这在解决特定问题方面具有各种局限性。在本文中，我们提出了一种使用方向标记回归的停车位检测方法，即DMPR-PS。 DMPR-PS而不是利用多个现成模型，而是使用专门设计的基于CNN的基于CNN的模型，用于定向标记点回归。给定环形视图图像I，模型预测I上每个标记点的位置，形状和方向。从标记点，可以使用几何规则轻松推断出来的停车位。 DMPR-PS优于基准数据集的最先进的竞争对手，精确率为99.42％，召回率为99.37％，同时在NVIDIA Titan XP上实现了12ms的实时检测速度。为了使结果可重复，源代码可在https://github.com/teoge/dmpr-ps上获得。

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