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.

This paper discusses an optical reflection-based method for equipment anomaly detection that enhances weak signals and has high sensitivity to abnormalities. Automatic warp knitting machine yarn breakage detection, which has become an acknowledged difficulty in the textile field, is achieved. To the best of our knowledge, this is the first time that visual inspection has been applied to yarn breakage detection in weaving. Furthermore, based on the periodicity of the yarn distribution and the periodic motion law of the machine, the combined wavelet and STL (Seasonal and Trend decomposition using LOESS) decomposition method is proposed for yarn breakage detection. Finally, the efficiency and accuracy of the proposed method are verified experimentally. Our research is a successful application of reflection characteristics to the anomaly detection of non-Lambertian objects, which has implications for the high-precision anomaly detection of precision equipment.

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