Computer vision builds a connection between image processing and industrials, bringing modern perception to the automated industrials. At the same time, defect detection based on deep learning has played an important role in automated detection. In this paper, an improved convolutional neural network CU-Net for fabric defect detection is proposed. In this method, the classical U-Net network was improved. On the basis of network size compression, attention mechanism is introduced and a new compound loss function is used for training. Using the public AITEX defect fabric data set as the test sample, the experimental result shows that the accuracy and recall of the proposed method are 98.3% and 92.7%, respectively. Compared with the highest scores of other detection methods, they are improved by 4.8% and 2.3%, which improves the detection accuracy of fabric defect significantly.

No abstract

Generative Adversarial Network (GAN), deemed as a powerful deep-learning-based silver bullet for intelligent data generation, has been widely used in multi-disciplines. Furthermore, conditional GAN (CGAN) introduces artificial control information on the basis of GAN, which is more practical for many specific fields, though it is mostly used in domain transfer. Researchers have proposed numerous methods to tackle diverse tasks by employing CGAN. It is now a timely and also critical point to review these achievements. We first give a brief introduction to the principle of CGAN, then focus on how to improve it to achieve better performance and how to evaluate such performance across the variants. Afterward, the main applications of CGAN in domain transfer are presented. Finally, as another major contribution, we also list the current problems and challenges of CGAN.

No abstract

No abstract