An improved image classification method considering rotation based on convolutional neural network

Abstract

Convolution Neural Network (CNN) is one of the most popular deep learning methods in recent years, which achieves great success in the field of image classification. In this paper, an improved image classification method considering rotation based on CNN is proposed. Essentially, convolution is only a method to smooth the image, which doesn’t consider the effect of image rotation any more. It can be proven that after some images are rotated 180 ◦, CNN can recognize them well while fail to recognize them before. So, rotation is one of the efficient ways to improve object recognization. Four kinds of typical CNN are adopted in this paper, which are CaffeNet, VGG16, VGG19 and GoolgeNet. It has been proven that the accurate rates are all increased no matter which one is adopted among these four CNN. This method proposed in this paper can recognize dangerous objects automatically with good performances.