Abstract
Deep learning technology is outstanding in visual inspection. However, in actual industrial production, the use of deep learning technology for visual inspection requires a large number of training data with different acquisition scenarios. At present, the acquisition of such datasets is very time-consuming and labor-intensive, which limits the further development of deep learning in industrial production. To solve the problem of image data acquisition difficulty in industrial production with deep learning, this paper proposes a data augmentation method for deep learning based on multi-degree of freedom (DOF) automatic image acquisition and designs a multi-DOF automatic image acquisition system for deep learning. By designing random acquisition angles and random illumination conditions, different acquisition scenes in actual production are simulated. By optimizing the image acquisition path, a large number of accurate data can be obtained in a short time. In order to verify the performance of the dataset collected by the system, the fabric is selected as the research object after the system is built, and the dataset comparison experiment is carried out. The dataset comparison experiment confirms that the dataset obtained by the system is rich and close to the real application environment, which solves the problem of dataset insufficient in the application process of deep learning to a certain extent.
Highlights
As one of the branches of machine learning, deep learning forms more abstract and high-level features by combining simple features at the bottom, so as to extract and represent complex features of input data [1]
The results showed that the use of generative adversarial networks (GAN)-based data augmentation techniques can increase the accuracy of emotion classification by 5% to
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-degree of freedom (DOF) automatic image acquisition and build a multi-DOF automatic image acquisition system for deep learning to solve the problem of difficult image acquisition
Summary
As one of the branches of machine learning, deep learning forms more abstract and high-level features by combining simple features at the bottom, so as to extract and represent complex features of input data [1]. In the above application fields, the image acquisition environment is complex, which needs to be shot in multi-pose and multi-illumination directions to obtain comprehensive datasets. It ensures that the final deep learning model adapts to various complex situations. 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.
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