Akademik Veri Yönetim Sistemi
Cumhuriyet Science Journal, cilt.38, sa.2, ss.342-354, 2017 (Hakemli Dergi)
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