Fabric Defect Segmentation System Based on a Lightweight GAN for Industrial Internet of Things

Abstract

Machine vision systems based on deep learning play an important role in the industrial Internet of things (IIoT) and Industry 4.0 applications, especially for product quality monitoring. Fabric defect detection is an important task in the industrial production of textiles and is crucial for product quality assurance. In actual production, the detection of many small and weak target defects remains challenging. Furthermore, industrial production requires high production rates and small model sizes in practice. This study proposes a lightweight segmentation system that meets real-time industrial production requirements. Herein, first, the defect sample image was repaired based on the image repair mechanism of the generative adversarial network model. Then, the difference between the defect sample and the repaired sample was obtained and subsequent processing, such as denoising and enhancement, was done. Finally, the defect areas were segmented. Our model was specifically designed for the segmentation of weak and small defects. This was achieved through adversarial training, optimization of an objective function, and image processing. Experimental comparisons show that the intersection over union of the three different datasets is 77.84%, 77.85%, and 73.6% and that our model is superior to the conventional semantic segmentation model. Furthermore, our model has good image restoration quality with a low mean absolute error and high structural similarity index. Additionally, our model is lightweight, has good real-time performance, and is suitable for applications in the IIoT and industrial production lines, such as embedded systems.