Abstract
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.
Highlights
Augmented reality is to superimpose computer-generated virtual objects, scenes and information with real scenes. ‘‘Enhanced’’ is to increase and strengthen understanding. ‘‘Reality’’ is a definition of real and existing things
The effect to be achieved by augmented reality is the fusion of virtual information and real scene [1]
Augmented reality technology is a fusion of virtual and real technology, which aims to accurately register computergenerated virtual information into real-time scene images collected in real time to form an enhanced image for display to users, thereby enhancing the user’s sensory enjoyment
Summary
Augmented reality is to superimpose computer-generated virtual objects, scenes and information with real scenes. ‘‘Enhanced’’ is to increase and strengthen understanding. ‘‘Reality’’ is a definition of real and existing things. Q. Cheng et al.: Augmented Reality Dynamic Image Recognition Technology Based on Deep Learning Algorithm data to learn more accurate feature information, and improves the recognition speed and accuracy of the overall model [10], [11]. The Caps Nets model uses a convolution structure for feature extraction, but Primary Caps (main capsule layer) can divide the data information into multiple units under multiple channels, thereby generating vectors that retain spatial information according to each unit. This structure replaces the pooling layer in the traditional convolutional network, which can effectively reduce the loss of information. After a series of operations such as convolution layer and pooling layer, the features of different layers can be obtained
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