基于深度学习的高速服务区车位检测算法

本文利用卷积神经网络对高速公路服务区停车场进行场景分割与车位检测.首先，通过扩充高速公路服务区停车场数据集，利用卷积神经网络进行高速公路服务区停车场区域分割与车辆检测，并对特征提取网络进行权重共享，从而达到联合训练的目的及网络模型轻量化.进而，通过对车辆的纹理特征提取，采用金字塔特征融合的方法对小目标的识别进行强化.最后，利用高速公路服务区停车位的先验知识实时计算停车场的停车位信息.实际应用表明该方法在复杂场景下，对车位检测的准确率为94%，检测速度为每秒25帧，具有很强的泛化能力，适合用于高速公路服务区停车场车位检测.

Research on Parking Space Recognition in Expressway Service Area Based on Convolutional Neural Network

In this study, the convolutional neural network is used to segment the scene and detect the parking space in the parking lot of the expressway service area. Firstly, the study expands the parking lot dataset of the expressway service area, and uses the convolutional neural network to segmentation of the highway parking lot and vehicle detection, the weighted neural network is used to share the weights of the feature extraction network to achieve the joint training and lightweight of the network model. Furthermore, the convolutional neural network enhances the recognition of small targets by extracting texture features of the vehicle and using pyramid feature fusion. Finally, the system uses the prior knowledge of the parking space in the expressway service area to calculate the parking space quantity information of the parking lot in real time. The practical application shows that the method has a accuracy of 94% for parking space detection and a detection speed of 25 frames per second in complex scenes. It has strong generalization ability and is suitable for parking lot detection.