Zhang Lin, Huang Junhao, Li Xiyuan, Xiong Lu
IEEE Trans Image Process. 2018 Jul 18. doi: 10.1109/TIP.2018.2857407.
In the automobile industry, recent years have witnessed a growing interest in developing self-parking systems. For such systems, how to accurately and efficiently detect and localize the parking-slots defined by regular line segments near the vehicle is a key and still unresolved issue. In fact, kinds of unfavorable factors, such as the diversity of ground materials, changes in illumination conditions, and unpredictable shadows caused by nearby trees, make the vision-based parking-slot detection much harder than it looks. In this paper, we attempt to solve this issue to some extent and our contributions are twofold. First, we propose a novel DCNN (Deep Convolutional Neural Networks) based parking-slot detection approach, namely DeepPS, which takes the surround-view image as the input. There are two key steps in DeepPS, identifying all the marking-points on the input image and classifying local image patterns formed by pairs of markingpoints. We formulate both of them as learning problems, which can be solved naturally by modern DCNN models. Second, to facilitate the study of vision-based parking-slot detection, a largescale labeled dataset is established. This dataset is the largest in this field, comprising 12,165 surround-view images collected from typical indoor and outdoor parking sites. For each image, the marking-points and parking-slots are carefully labeled. The efficacy and efficiency of DeepPS have been corroborated on our collected dataset. To make our results fully reproducible, all the relevant source codes and the dataset have been made publicly available at https://cslinzhang.github.io/deepps/.
在汽车行业，近年来人们对开发自动泊车系统的兴趣日益浓厚。对于此类系统，如何准确、高效地检测和定位车辆附近由规则线段定义的停车位是一个关键且尚未解决的问题。事实上，诸如地面材料的多样性、光照条件的变化以及附近树木造成的不可预测的阴影等各种不利因素，使得基于视觉的停车位检测比看起来要困难得多。在本文中，我们试图在一定程度上解决这个问题，我们的贡献有两个方面。首先，我们提出了一种基于深度卷积神经网络（DCNN）的新型停车位检测方法，即深度停车位检测（DeepPS），它以环视图像作为输入。DeepPS 有两个关键步骤，识别输入图像上的所有标记点，并对由成对标记点形成的局部图像模式进行分类。我们将这两个步骤都表述为学习问题，现代 DCNN 模型可以自然地解决这些问题。其次，为了便于基于视觉的停车位检测研究，我们建立了一个大规模的标注数据集。这个数据集是该领域最大的，包含从典型的室内和室外停车场收集的 12165 张环视图像。对于每张图像，都仔细标注了标记点和停车位。在我们收集的数据集上验证了 DeepPS 的有效性和效率。为了使我们的结果完全可重现，所有相关的源代码和数据集都已在 https://cslinzhang.github.io/deepps/ 上公开提供。
