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This paper proposes a novel data-enhanced virtual texture generation network for use in deep learning detection systems. The current methods of data enhancement, such as image flipping, scaling ratios, or Generative Adversarial Networks, have limitations as they cannot determine characteristics beyond the training data. The proposed system uses the texture characteristics of a learning surface to generate surface textures through the Open Graphics Library, which can simulate material textures, light sources, and shadow effects. This enables the generation of required texture parameters for the Reinforcement Learning Network to conduct parameter search. The generated image is authenticated by a discriminator, and the reward score is fed back into the critic network to update the value network. The proposed system can complement the imbalance of defective data types, generate large quantities of random and non-defective data, and automatically classify and label during the generation process, reducing labor consumption and improving labeling accuracy. The study found that the proposed data enhancement method can increase the diversity of data characteristics, and the generated data can increase the recall rate of test and verification data sets. Specifically, the proposed system increased the recall rate of test data sets with different distributions from 78.21% to 82.40% and the recall rate of verification data sets with the same distribution from 81.64 to 91.94%. [ABSTRACT FROM AUTHOR]

In this research, a simulation system based on a physical model and its lighting feature is developed to perform three-dimensional model creation, and graphics software is used to randomly generate a simulated surface with defects, which also cooperates with the virtual environment to reproduce the original environment. Furthermore, the use of a generative adversarial network to optimize the virtual dataset created symmetrically by the system is studied in order to reduce the effect of the difference between the real and virtual images. This system compensates for the condition of data imbalance occurring between qualified products and defective products in the production line, and a large amount of random data with and without defects can be created. In addition, the process of the database creation is classified and marked, such that complicated and time-consuming preliminary steps can be reduced; therefore, the data collection cost can be significantly reduced and the uncertainly associated with manual operation is also reduced. When a simulated textured surface generated from this system is used to perform training, the inspection background accuracy reaches 98%, and the accuracy also reaches 78% in real defect inspection process; therefore, the location of the defect can be determined completely. [ABSTRACT FROM AUTHOR]

This research is aimed to detect defects on the surface of the fabric and deep learning model optimization. Since defect detection cannot effectively solve the fabric with complex background by image processing, this research uses deep learning to identify defects. However, the current network architecture mainly focuses on natural images rather than the defect detection. As a result, the network architecture used for defect detection has more redundant neurons, which reduces the inference speed. In order to solve the above problems, we propose network pruning with the Bayesian optimization algorithm to automatically tune the network pruning parameters, and then retrain the network after pruning. The training and detection process uses the above-mentioned pruning network to predict the defect feature map, and then uses the image processing flow proposed in this research for the final judgment during fabric defect detection. The proposed method is verified in the two self-made datasets and the two public datasets. In the part of the proposed network optimization results, the Intersection over Union (IoU) of four datasets are dropped by 1.26%, 1.13%, 1.21%, and 2.15% compared to the original network model, but the inference time is reduced to 20.84%, 40.52%, 23.02%, and 23.33% of the original network model using Geforce 2080 Ti. Furthermore, the inference time is also reduced to 17.56%, 37.03%, 19.67%, and 22.26% using the embedded system AGX Xavier. After the image processing part, the accuracy of the four datasets can reach 92.75%, 94.87%, 95.6%, and 81.82%, respectively. In this research, Yolov4 is also trained with fabric defects, and the results showed this model are not conducive to detecting long and narrow fabric defects. [ABSTRACT FROM AUTHOR]