LAUSR

Fabric quality governing and defect detection are playing a crucial role in the textile industry with the development of high customer demand in the fashion market. This work presents fabric defect detection using the sensitive plant segmentation algorithm (SPSA) which, is developed with the sensitive behaviour of the plant biologically named “Mimosa pudica”i. This method consists of two stages. The first stage enhances the contrast of the defective fabric image and the second stage segments the fabric defects with aid of SPSA. The proposed work SPSA is developed for defective pixels identification in both uniform and non-uniform patterns of fabrics. In this work, SPSA has been done by checking with devised condition, correlation and error probability. Every pixel will be checked with the developed algorithm, to get marked either defective or non-defective pixels. The proposed SPSA has been tested on the different types of fabric defect databases and shows a prodigious performance over existing methods like the Differential evolution based optimal Gabor filter model (DEOGF), Gabor filter bank (GFB), Adaptive sparse representation-based detection model (ASR) and Fourier and wavelet shrinkage (FWR).