## 1 Introduction
First, a crop box was randomly generated to crop the corresponding position of the A picture, and then the region of interest (ROI) of the corresponding position of the B picture was placed in the cropped area of the A picture to form a new sample. The method based on a neural network is to generate new image samples based on the original data through the neural network models such as variational autoencoder (VAE) [24] and generative adversarial networks (GAN) [25]. [PERSON] et al. [26] proposed a data augmentation method using GAN. The results showed that the use of GAN-based data augmentation techniques can increase the accuracy of emotion classification by 5% to 10%. However, though a large number of data can be generated by data augmentation, augmented data are not always representative enough due to the complexity of the shooting environment, conditions, and methods, such as the illumination diversity of the data in practical application, resulting in that the final model has low accuracy in real data detection. In summary, in the field of deep learning, image data acquisition is still a key technical difficulty and has not been solved well. In view of the insufficient datasets in the practical industrial application of deep learning, we propose a data augmentation method for deep learning based on multi-DOF automatic image acquisition and build a multi-DOF automatic image acquisition system for deep learning to solve the problem of difficult image acquisition. The system can comprehensively simulate the actual image acquisition situation and quickly establish a large number of accurate and real training data. The simulation of the real acquisition situation is carried out through changing the imaging's main factors which are shooting angles and illumination conditions. The system mainly involves two key technologies: The first is to ensure the camera can shoot the object through the calculation of the camera's spatial pose accurately. The other is to optimize the spatial random acquisition path to acquire image data rapidly. In order to verify the authenticity and richness of the dataset obtained by the system, we took fabrics as the research object, then used the fabric data collected by the system and the fabric data collected under ideal conditions to conduct fabric classification experiments. The results show that the model trained by the system collected dataset can recognize fabrics in different scenes, and the recognition accuracy rate was more than 91%. It shows that the images obtained by the system were more abundant including all kinds of actual acquisition situations. The structure of the paper is as follows: Section 2 proposes a method of the multi-DOF image automatic acquisition and designs the system architecture. Furthermore, the process of the acquisition system is also introduced. Section 3 includes two key technologies: camera pose calculation and random acquisition path optimization. By comparing two methods of calculating camera pose, the formula of the camera pose is determined. In Section 4, dataset acquisition and dataset comparison experiments were carried out to verify the representativeness of images collected by this system. Section 5 concludes the paper.