Fabric Defect Detection Using Deep Convolutional Neural Network
Maheshwari Biradar, B. G. Shiparamatti, Pradeep M. Patil
Abstract
The enormous growth in the fashion industry increased the demand for quality of service of the fabric material. Fabric defect detection plays a crucial role in maintaining the quality of service as a single defect in the fabric can halve its price. Traditional machine learning approaches are less generalized and cannot be employed for fabric defect detection of patterned as well as non-patterned fabrics. This paper presents Deep Convolutional Neural Network (DCNN) for fabric defect detection. The proposed method consists of a three-layered DCNN for the representation of the normal and defected fabric patch. The performance of the proposed DCNN is evaluated on the standard TILDA and in-house database using percentage accuracy. It is noticed that the proposed method gives an accuracy of 98.33 and 90.39% for patterned and non-patterned fabric defect detection for in-house database and 99.06% accuracy for non-patterned TILDA database.