TY  - JOUR
TI  - Smart fabric inspection using Mimosa pudica plant
AU  - Nisha, M. Fathu
AU  - Malliga, L.
AU  - Periannasamy, S. Manthandi
AU  - Bennet, J. John
AU  - Mary Rajee, S. Amalorpava
T2  - Industria Textila
AB  - 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).
DA  - 2023/05/02/
PY  - 2023
DO  - 10.35530/IT.074.02.1719
DP  - DOI.org (Crossref)
VL  - 74
IS  - 02
SP  - 163
EP  - 168
SN  - 12225347
UR  - http://revistaindustriatextila.ro/images/2023/2/04%20FATHU%20NISHA%20INDUSTRIA%20TEXTILA%20no.2_2023.pdf
Y2  - 2023/05/07/13:51:05
ER  -