Fabric Defect Detection Using Deep Convolutional Neural Network

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.