@article{MAKHILLAJIT20141365833,
    title = {An Intelligent System for Automatic Fabric Inspection},
    journal = {Asian Journal of Information Technology},
    volume = {13},
    number = {6},
    pages = {308-312},
    year = {2014},
    issn = {1682-3915},
    doi = {ajit.2014.308.312},
    url = {https://makhillpublications.co/view-article.php?issn=1682-3915&doi=ajit.2014.308.312},
    author = {G.M. and},
    keywords = {:Artificial Neural Network (ANN),fault detection,image processing,Back Propagation algorithm,feature extraction},
    abstract = {Quality inspection is one of the major problem for fabric 
  manufacturers in textile industries. Textile manufacturing is a process of converting 
  various types of fibers into yarn, woven then into fabric. Weaving is a process 
  of interlacing two distinct yarns namely warp and weft. A fabric fault is any 
  abnormality in the fabric that hinders its acceptability by the user. At present, 
  the fault detection is done manually after production of a sufficient amount 
  of fabric. The nature of work is very dull and repetitive. There is a possibility 
  of human errors with high inspection time in manual inspection, hence it is 
  uneconomical. This study proposed a computer based inspection system for identification 
  of defects in the woven fabrics using image processing and Artificial Neural 
  Network (ANN) with benefits of low cost and high detection rate. The defects 
  consist of hole, stain, warp float and weft float. The inspection system first 
  acquires high quality vibration free images of the fabric. Then, the acquired 
  images are first normalized and preprocessed using image processing techniques 
  then the preprocessed image is converted into binary images based on the threshold 
  value. From the binary image features are extracted and these extracted features 
  are given as input to the Artificial Neural Network (ANN) which uses Back Propagation 
  algorithm to calculate the weighted factors and generates the output. The ANN 
  is trained by using 115 defect free and defected images.}
    }