Crowd density detection algorithm of contextual attention convolutional neural network based on adversarial learning

The invention belongs to the technical field of crowd density detection, and particularly relates to a crowd density detection algorithm of a contextual attention convolutional neural network based on adversarial learning, which comprises the following steps: S1, selecting a training set, reading pictures in the crowd density data set, and setting training parameters of the convolutional neural network model; s2, training the convolutional neural network model by taking loss function minimization as a target according to the convolutional neural network model and training parameters thereof. A group of expansion convolution is connected in a denser mode through a deep learning means, the crowd density detection effect is improved under the condition that the size of the model is not obviously increased, and the efficiency of the model is also improved by using an attention mechanism. 本发明属于人群密度检测方面的技术领域，具体为基于对抗学习的上下文注意力卷积神经网络的人群密度检测算法，该基于对抗学习的上下文注意力卷积神经网络的人群密度检测算法包括以下步骤：S1：选取训练集，读取人群密度数据集中的图片，并设置卷积神经网络模型的训练参数；