电子测量与仪器学报

单凯强,桑海峰.基于全景视频下标记点特征的停车位检测技术研究[J].电子测量与仪器学报,2022,36(2):203-210

基于全景视频下标记点特征的停车位检测技术研究

Research on parking slot detection technology based onthe mark point of panorama video

DOI：

英文关键词:automatic parking parking slot detection multi-scale feature mark point deep learning

基金项目:国家自然科学基金（61773105）、辽宁省教育厅科研项目（LJGD2020006）资助

作者	单位
单凯强	1.沈阳工业大学信息科学与工程学院
桑海峰	1.沈阳工业大学信息科学与工程学院

Author	Institution
Shan Kaiqiang	1.School of Information Science and Engineering, Shenyang University of Technology
Sang Haifeng	1.School of Information Science and Engineering, Shenyang University of Technology

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中文摘要:

在车载全景系统中,如何准确地检测停车位的位置和车位的方向仍然是一个待解决的问题。针对这一问题,设计了一个双路并行多尺度标记点检测网络,双路网络分别用于检测标记点的位置和角度。对全景图像提取多尺度的特征,并行维护一高一低两个分辨率的分支网络,两个分支互相融合,高分辨率特征以高斯热图的形式表述标记点的位置。提出了一种新的停车位方向计算方法,使用两个标记点的方向以及两个标记点的相对位置计算停车位的方向。为验证所提方法的可行性,使用公共数据集 PS2. 0 的训练集训练所设计的网络,在公共数据集 PS2. 0 和自行采集的数据集 PSS 上分别测试的停车位检测精确度为 99. 4%、95. 27%,召回率为 99. 88%、80. 89%,在 PS2. 0 上标记点位置平均误差为 0. 84 pixel,车位方向的误差为 0. 71°。实验结果表明,与现有方法相比,所提出的车位检测网络降低了标记点定位和车位方向的误差,且在 PSS 数据集上有较强的泛化能力。

英文摘要:

In the vehicle panoramic system, how to accurately detect the position and direction of parking slot is still a problem to be solved. To solve this problem, we designed a two-way parallel multi-scale mark point detection network. The two-way network is used to detect the position and angle of mark point respectively. Multi-scale features are extracted from panoramic images, and a branch network of one high and one low resolution is maintained in parallel. The two branches are fused with each other. The high-resolution features express the location of mark point in the form of Gaussian heatmap. A new method for calculating the direction of parking slots is proposed, which uses the direction of two mark points and the relative position of two mark points to calculate the direction of parking slot. In order to verify the feasibility of the proposed method, the designed network was trained using the training set of the public dataset PS2. 0, and the parking slot detection precision tested on the public dataset PS2. 0 and the self-collected dataset PSS is 99. 4% and 95. 27%, the recall is 99. 88% and 80. 89%, the average error of the mark point position on PS2. 0 is 0. 84 pixel, and the error of the parking direction is 0. 71 degree. The experimental results show that compared with the existing methods, the parking slot detection network proposed reduces the errors in the location of the mark point and the direction of the parking slot, and has a strong generalization ability on the PSS dataset.

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