Augmented Reality Dynamic Image Recognition Technology Based on Deep Learning Algorithm
PubDate: July 2020
Teams: Zhengzhou University
Writers: Qiuyun Cheng; Sen Zhang; Shukui Bo; Dengxi Chen; Haijun Zhang
Augmented reality is a research hotspot developed on the basis of virtual reality. Friendly human-computer interaction interface makes the application prospect of augmented reality technology very broad. Convolutional neural networks in deep learning have been widely used in the field of computer vision and become an important weapon in dynamic image recognition tasks. Combining deep learning and traditional machine learning techniques, this article uses convolutional neural networks to extract features from image data. The convolutional neural network uses the last layer of features and uses the softmax recognizer for recognition. This article combines a convolutional neural network that can learn good feature information with integrated learning that has good recognition effects. In the recognition tasks of the MNIST database and the CIFAR-10 database, comparison experiments were performed by adjusting the hierarchical structure, activation function, descent algorithm, data enhancement, pooling selection, and number of feature maps of the improved convolutional neural network. The convolutional neural network uses a pooling size of 3*3, and uses more cores (above 64), small receptive fields (2*2), and more hierarchical structures. In addition, the Relu activation function, gradient descent algorithm with momentum, and enhanced data set are also used. The research results show that under certain experimental conditions, the dynamic image recognition results have dropped to a very low error rate in the MNIST database, and the error rate in the CIFAR-10 database is also ideal.