Deep Learning for DENIM Defects Detection

Fabric defects can be attributed to various factors such as raw material, spinning, weaving, knitting, and dyeing processes. These factors are the main causes of fabric defects, which are a major quality issue in the garment industry. The demand for high-quality fabrics has increased, and customers are now more aware of “non-quality” issues, leading to higher quality requirements. In this paper, we propose a deep learning approach based on neural networks for defect detection. In fact, we have developed a new methodology for detecting DENIM weaving defects to facilitate fast, efficient, and accurate decision making. Our neural network model is based on the VGG16 architecture, which generates convolutional feature maps. A classifier was used for our specific classification case. The proposed methodology was tested on a dataset containing a large number of images with and without defects. This approach showed an excellent overall model accuracy of 98.52%.