Convolutional Autoencoder Based Textile Defect Detection Under Unconstrained Setting
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Automated visual defect detection on textile products under unconstrained setting is a much sought-after, and at the same time a challenging problem. In general, textile products are structurally complex and highly varied in design, which makes the development of a generalized approach using conventional image processing methods impossible. Deep supervised machine learning models have been very successful on similar problems but cannot be applied in this use-case due to lack of annotated data. This paper demonstrates a novel automated approach which still leverages on the ability of deep learning models to capture complex features on the textured and colored fabric, but in an unsupervised manner. Specifically, deep autoencoders are applied to capture the complex features, which are further processed by image processing techniques like thresholding and blob detection, subsequently leading to detection of defects in the images.