A Review of Image Recognition with Deep Convolutional Neural Network

Abstract

Image recognition technology is widely used in industry, space military, medicine and agriculture. At present, most of the image recognition methods use artificial feature extraction which is not only laborious, time consuming, but also difficult to do. Deep convolutional neural network is becoming a research hotspot in recent years. It has successfully applied to character recognition, face recognition, and so on. The traditional deep convolutional neural network still has some defaults when dealing with large-scale images and high-resolution complex images. So many research works are rolling ahead to improve the network to make it more efficient and robust. Firstly, the principle of the traditional convolutional neural network was briefly introduced. Then, the improvements on convolutional layer, pooling layer, activation function of convolutional neural network in recent years were summarized. Its applications in image recognition were also presented. Finally, the challenges in convolutional neural network research were analyzed and our recent works ware introduced.