Autoencoder-Based Fabric Defect Detection with Cross- Patch Similarity.

Fabric quality inspection plays an important role in the textile industry. As an effective approach to learn data representations, autoencoder has been adopted for defect detection. With the basic idea that the defect area cannot be recovered by the model trained on non-defective image patches, the residual is often used as an indication for defect judgement. However, usually the texture (non-defect) area in a defective patch also cannot be well reconstructed, which makes the pixel-wise detection inaccurate. In this paper, by exploring similarities between different patches in the whole test image, a novel autoencoder-based fabric defect detection method is proposed. In order to maintain the texture area in the reconstructed patch, the original encoded latent variable is modified, and the cross-patch similarity is introduced for determining the modification function. The whole algorithm is conducted in an iterative way, and the detection results will become better and better. Experimental results on the benchmark datasets demonstrate the effectiveness of our proposal.