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.