  Nowadays, Deep Convolutional Neural Networks (DCNNs) are widely used in
fabric defect detection, which come with the cost of expensive training and
complex model parameters. With the observation that most fabrics are defect
free in practice, a two-step Cascaded Zoom-In Network (CZI-Net) is proposed for
patterned fabric defect detection. In the CZI-Net, the Aggregated HOG (A-HOG)
and SIFT features are used to instead of simple convolution filters for feature
extraction. Moreover, in order to extract more distinctive features, the
feature representation layer and full connection layer are included in the
CZI-Net. In practice, Most defect-free fabrics only involve in the first step
of our method and avoid a costive computation in the second step, which makes
very fast fabric detection. More importantly, we propose the
Locality-constrained Reconstruction Error (LCRE) in the first step and
Restrictive Locality-constrained Coding (RLC), Bag-of-Indexes (BoI) methods in
the second step. We also analyse the connections between different coding
methods and conclude that the index of visual words plays an essential role in
the coding methods. In conclusion, experiments based on real-world datasets are
implemented and demonstrate that our proposed method is not only
computationally simple but also with high detection accuracy.
