Fabric Defect Detection System

Fabric inspection is very significant in textile manufacturing. Quality of fabric defends on vital activities of fabric inspection to detect the defects of fabric. Profits of industrialists have been decreased due to fabric defects and cause disagreeable loses. Traditional defect detection methods are conducted in many industries by professional human inspectors who manually draw defect patterns. However, such detection methods have some shortcomings such as exhaustion, tediousness, negligence, inaccuracy, complication as well as time-consuming which cause to reduce the finding of faults. In order to solve these issues, a framework based on image processing has been implemented to automatically and efficiently detect and identify fabric defects. In three steps, the proposed system works. In the first step, image segmentation has been employed on more than a few fabric images in order to enhance the fabric images and to find the valuable information and eliminate the unusable information of the image by using edge detection techniques. After the first step of the paper, morphological operations have been employed on the fabric image. In the third step, feature extraction has been done through FAST (Features from Accelerated Segment Test) extractor. After feature extraction, If PCA (Principal Component Analysis) is applied as it reduces the dimensions and preserves the useful information and classifies the various fabric defects through a neural network and used to find the classification accuracy. The proposed system provides high accuracy as compared to the other system. The investigation has been done in a MATLAB environment on real images of the TILDA database.