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.
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Acknowledgments
The authors thank The Natural Science Foundation of Hebei Provence for their financial support (F2015201033), the Natural Science Foundation of Hebei Provence for their financial support (F2017201069). The authors also thank Information Technology Center of Hebei University for providing the high performance computing platform.
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Liu, Q. et al. (2017). A Review of Image Recognition with Deep Convolutional Neural Network. In: Huang, DS., Bevilacqua, V., Premaratne, P., Gupta, P. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10361. Springer, Cham. https://doi.org/10.1007/978-3-319-63309-1_7
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