Fabric Defect Detection Using Convolutional Neural Network

Eldho Paul, K. Nivedha, M. Nivethika, V. Pavithra and G. Priyadharshini

Fabric defect detection is used to analyze the quality control in the garments. The efficiency of the Traditional inspection is low and hence supervised learning is used. “The work tells about supervised learning-based automated approach to detect and identify the fabric defects without any manual intervention by using deep learning method”[15]. Considering all these previous outbreaks, the proposed work is implemented by using Convolution Neural Network (CNN) algorithm for automatic prediction of the fabric defect. Here, the present work has divided into four stages. First the data has been collected. Then, data waggling (pre-processing the collected data) i.e., in this process the unwanted data are removed. After analyzing the dataset, it has been trained and tested. Finally, the CNN algorithm is applied to classify the images and identify the defect information. Thus the proposed work gives high accuracy and effective prediction of fabric defect with minimum time. The proposed work have an accuracy rate of 82%.

Volume 12 | 05-Special Issue

Pages: 950-955

DOI: 10.5373/JARDCS/V12SP5/20201840