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首页> 外文期刊>IEEE Transactions on Intelligent Transportation Systems >Convolutional Neural Networks for On-Street Parking Space Detection in Urban Networks
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Convolutional Neural Networks for On-Street Parking Space Detection in Urban Networks

机译：卷积神经网络在城市网络路边停车位检测中的应用

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摘要

The purpose of this paper is the development of data science models for the detection of empty on-street parking spaces in urban road networks based on data provided by in-vehicle cameras that are already, or soon will be, a standard vehicle equipment. A rolling spatial interval is used to identify the existence of an on-street parking space and the properties of empty spaces are used to determine the availability of the parking space. Convolutional neural networks are developed, trained, and evaluated with the use of images from a moving vehicle camera. The images are preprocessed and converted to suitable matrices, so that only the useful information for the empty on-street parking space detection problem is preserved. The optimized convolutional networks, in terms of structural and learning parameters, provided predictions for the detection of empty on-street parking spaces with approximately 90 average accuracy. The proposed model performs better than the relatively complex SVMs, which supports its appropriateness as an approach. Finally, the implementation of a framework, which integrates the developed models to produce meaningful parking information for drivers in real time, is discussed.
机译：本文的目的是根据已经或即将成为标准车辆设备的车载摄像机提供的数据，开发用于检测城市道路网络中空路旁停车位的数据科学模型。滚动空间间隔用于标识路旁停车位的存在，空位的属性用于确定停车位的可用性。卷积神经网络是通过使用来自移动车辆摄像头的图像来开发，训练和评估的。对图像进行预处理并将其转换为合适的矩阵，以便仅保留有关空路上停车位检测问题的有用信息。在结构和学习参数方面，优化的卷积网络为检测空的路边停车位提供了大约90个平均精度的预测。所提出的模型比相对复杂的SVM更好，后者支持其适当性。最后，讨论了一个框架的实现，该框架集成了开发的模型以实时为驾驶员提供有意义的停车信息。

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