Stacked Autoencoder Method for Fabric Defect Detection.


Creative Commons License

Şeker A. , Yüksek A. G.

Cumhuriyet Science Journal, cilt.38, ss.342-354, 2017 (Diğer Kurumların Hakemli Dergileri)

  • Cilt numarası: 38 Konu: 2
  • Basım Tarihi: 2017
  • Doi Numarası: 10.17776/cumuscij.300261
  • Dergi Adı: Cumhuriyet Science Journal
  • Sayfa Sayıları: ss.342-354

Özet

The fabric defect detection has crucial importance in terms of sectoral quality. As fabric defection stage, accordingly the growing market volume and production capacity, detection via human vision has caused largely time-wasting and success rate decreasing until 60%. Due to a fabric has unique texture, there is necessity for it to work on separately from other images types while extracting its features. Features are vital material of computer vision especially classification problems. Hence, extracting right features is the most significant stage of error detection. This purpose in mind on this study, deep learning which distinguishes with multi-layer architectures and reveals high achievement on image and speech procession recent years by self-feature extraction is applied to fabric defect detection. Stacked autoencoder -a deep learning method- that aimed to represent input data via compression or decompression is tried to detect defect of fabrics and it gained acceptable success. The principal aim of this study is to increase achievement of feature extraction by tuning up the input value and hyper parameters autoencoder. Thanks to the fine tuning of hyper-parameters of deep model, we have 96% success rate on our own dataset