Researches advanced in image recognization based on deep learning

Abstract

Image recognition is a fundamental problem in the field of computer vision community, which aims to understand the content of an image to predict the class of the image. Traditional image recognition methods rely heavily on the quality of handcrafted features, and the recognition accuracy and generalization ability cannot meet practical application requirements. Thanks to the speedy progress of deep learning theory and technology, convolutional neural networks can adaptively learn image semantic features through high-dimensional nonlinear changes, which sensibly rises the exactness of image recognition. At present, image recognition has been generally applied to many fields such as commodity circulation, smart retail, pest identification, medical image analysis and so on. In this paper, we introduce the latest research progress in deep learning-based image recognition. Specifically, following the temporal clues of technological development, we first introduce representative image recognition networks, including their design ideas, basic network structures, advantages and disadvantages, etc. Second, we quantitatively compare the performance of different image recognition networks. Finally, we summarize the existing problems in the sphere of image recognition research and discuss its possible future directions.