基于非局部操作的深度卷积神经网络车位占用检测算法

随着城市交通智能化发展，准确高效地获取可用车位对于解决日益严峻的停车难问题至关重要。该文提出一种基于非局部操作的深度卷积神经网络车位占用检测算法。针对停车位图像特性，引入非局部操作，度量远距离像素间的相似性，直接获取边缘高频特征；使用小卷积核获取局部细节特征；以端到端的方式训练网络。实验中，通过设置不同卷积核尺寸和非局部模块层数，优化网络结构。实验结果表明，该文所提算法与传统的基于纹理特征的车位占用检测算法相比，无论在预测精度还是模型的泛化性能，均具有显著的优势。与当前广泛应用的基于局部特征提取的卷积神经网络相比，该算法具有较大的优势。在真实场景中，该算法同样具有较高精度，具备实际应用价值。

Deep Convolutional Neural Network for Parking Space Occupancy Detection Based on Non-local Operation

With the intelligent development of urban traffic, accurate and efficient access to available parking spaces is essential to solve the increasingly difficult problem of parking difficulties. Therefore, this paper proposes a deep convolutional neural network parking occupancy detection algorithm based on non-local operation. For the image characteristics of parking spaces, non-local operations are introduced, the similarity between distant pixels is measured, and the high-frequency features of the edges are directly obtained. The local details are obtained by using small convolution kernels, and the network is trained in an end-to-end manner. In the experiment, the network structure is optimized by setting different convolution kernel sizes and non-local module layers. The experimental results show that compared with the traditional texture feature-based parking space occupancy detection algorithm, the proposed algorithm has significant advantages in both prediction accuracy and generalization performance of the model. At the same time, compared with the currently widely used convolutional neural network based on local feature extraction, the algorithm also has great advantages. In real scenes, the algorithm also has high precision and has practical application value.