Research Information System
NEURAL COMPUTING AND APPLICATIONS, vol.37, pp.23249-23272, 2025 (Scopus)
The integration of artificial intelligence and image processing for fabric defect detection is gaining prominence due to its practical significance in enhancing production quality. This study proposes a fast and accurate convolutional neural network (CNN) designed to detect defects in fabric with minimal computational complexity. The model processes input images of size 256 × 256 and generates defect masks of size 64 × 64. To improve detection accuracy, the model incorporates techniques such as ResNet, scheduled learning rate policies, data augmentation, and a weighted cross-entropy loss function. Trained on a diverse dataset of 2,681 defect samples from four fabric types and defect classes (holes, oil stains, color stains, and roller marks), the model achieved an accuracy of over 96%, a loss value below 0.1, and high recall, precision, and F1-Score. Compared to other state-of-the-art models, the proposed model delivers competitive performance with significantly faster prediction times, making it suitable for real-world fabric inspection applications.