电子测量与仪器学报

汪家州,桑海峰.融合停车线方向的端到端停车位检测网络[J].电子测量与仪器学报,2024,38(1):86-93

融合停车线方向的端到端停车位检测网络

End to end parking slot detection network integrated with parking line direction

DOI：

英文关键词:parking slot detection direction of parking line end-to-end key point cross attention parking slot occupancy

基金项目:国家自然科学基金(62173078)、辽宁省自然科学基金(2022-MS-268)项目资助

作者	单位
汪家州	沈阳工业大学信息科学与工程学院沈阳110870
桑海峰	沈阳工业大学信息科学与工程学院沈阳110870

Author	Institution
Wang Jiazhou	School of Information Science and Engineering, Shenyang University of Technology, Shenyang 110870, China
Sang Haifeng	School of Information Science and Engineering, Shenyang University of Technology, Shenyang 110870, China

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中文摘要:

智能汽车自动泊车功能的基本要求之一是能够快速准确地检测出未被占用的停车位。针对这一问题，设计了一种融合停车线方向与全局特征的端到端可训练的停车位检测网络。首先提取出车位关键点的坐标以及关键点对应停车线的方向，并从图像全局特征中使用关键点的坐标提取出局部特征。将关键点信息、局部特征、全局特征使用交叉注意力机制融合，利用入口线鉴别器推断出关键点的停车位构成关系。根据关键点的停车位构成关系以及停车线方向，裁剪出停车位的区域图像并送入定制的车位占用分类网络进行分类，得到车位的占用信息。本文提出的方法在公共基准数据集PS2.0上进行了实验，其中该方法对矩形停车位的检测精度为99.65%，对倾斜停车位的检测精度为99.04%，在单块GPU上能够达到80 fps的检测速率。经验证，所提出的方法可以实时高精度的检测停车位位置、方向以及占用情况。

英文摘要:

One of the basic requirements for automatic parking of smart cars is to quickly and accurately detect unoccupied parking slots. To address this issue, an end-to-end train detection network that integrates the direction of the parking line with global features was designed. First, the coordinates of key parking spots and the direction of the corresponding parking line are extracted, and local features are extracted from the global features using the coordinates of the key spots. Integrate key point information, local features, and global features using the cross-attention mechanism, and use the entrance line discriminator to infer the composition relationship of key points’ parking slots. Based on the composition relationship of key points and the direction of the parking line, the regional image of the parking slot is cropped and sent to a customized parking slot occupancy classification network for classification, resulting in the occupancy information of the parking slot. The proposed method was tested on the public benchmark dataset PS2.0, where the detection accuracy of the method for rectangular parking slots was 99.65%, and for tilted parking slots was 99.04%. The detection rate of 80 frames per second was achieved on a single GPU. It has been verified that the proposed method can detect the location, direction, and occupancy of parking slots in real time with high accuracy.

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