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@article{179622,
        author = {TATIPARTHI SRAVANI and CHICHILI TEJASWINI REDDY and PARVATHALA HARINI and SANDRA CHAITHANYA},
        title = {fabric Defect Detection Using Vision Transformer Algorithm},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {7444-7448},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=179622},
        abstract = {Detecting defects in fabric materials is 
essential for ensuring product quality and reliability 
across various industrial applications. Conventional 
defect detection methods are often labor-intensive, 
time-consuming, and susceptible to human error. 
However, advancements in deep learning have paved 
the way for automated solutions that significantly 
enhance accuracy and efficiency. This study presents a 
fabric Defect Detection system utilizing a custom 
designed deep neural network inspired by the VIT 
architecture. The model integrates novel attention 
layers, which have not been previously incorporated 
into similar architectures, to improve predictive 
performance. 
Additionally, 
data 
augmentation 
techniques are employed to enhance the model’s 
ability to generalize and accurately detect defects in 
complex and subtle patterns. The proposed multi-class 
semantic segmentation model achieves an accuracy 
exceeding 91%, making it a viable solution for 
automating the defect detection process. This 
automation substantially reduces inspection costs and 
time, optimizing industrial workflows.},
        keywords = {Deep learning, Residual Network, Attention  layers, augmentation, fabric Defect,VIT},
        month = {May},
        }