Artificial Neural Network Models for Image Recognition

Abstract

Image recognition is an important application of artificial intelligence. Due to the continuous development of chips and algorithms, this field has made great progress in the past decade. At the same time, AI based image recognition has been widely used in the emerging fields like robotics, autonomous vehicles, and surveillance cameras, and their demand for AI has promoted the development of image recognition technology. Resent research has found that the convolutional neural network model is particularly effective for image classification and detection and smaller convolution kernels with deeper network structures are conducive to improving the accuracy. However, problems such as overfitting and activation function gradient descent need to be solved during the operation process. The latest convolutional neural network model ResNet applies residual units to reduce the redundant calculations and improve the efficiency of the model. In general, different variants of convolutional neural network structures have different effects on image recognition, but regional convolutional neural network structures are preferred in engineering applications for its balance between processing speed and recognition accuracy.