Rocket Image Classification Based on Deep Convolutional Neural Network

Abstract

In the field of aerospace measurement and control field, optical equipment generates a large amount of image data. Thus it has great research value for how to process a huge number of images data quickly and effectively. With the development of deep learning, great progress has been made in the task of image classification. This paper attempts to classify the task images generated by optical measurement equipment using the deep learning method. Firstly, based on residual network, a general deep learning image classification framework, a binary image classification network namely rocket image and other image is built. Secondly, on the basis of the binary cross entropy loss function, the modified loss function is used to achieves a better generalization effect on those images difficult to classify. Then, the 2689 visible image data downloaded from optical equipment is randomly divided into training set, validation set and test set. The data augmentation method is used to train the binary classification model on a relatively small training set. The optimal model weight is selected according to the loss value on the validation set. Finally, the trained model achieved a 100% precision and a 83.33% recall on the test set of 97 images which include 24 rockets images. This paper has certain value for exploring the application of deep learning method in the intelligent and rapid processing of optical equipment task image in aerospace measurement and control field.