Fabric defect detection via small scale over-complete basis set

Abstract

Defect detection has been a focal point in fabric inspection research and remains challenging. In this paper, a novel method for fabric defect detection is presented. In the proposed algorithm, only defect-free fabric images are used to build the over-complete basis set via sparse coding. Compared to traditional defect detection methods via sparse coding, our method uses a Gabor filter to reduce the complexity of the fabric signal, and takes the fabric patch’s projections in the small scale over-complete basis set as the original features, not the sparse representation. We compare the averages of the patch and its neighborhoods’ features with the standard features, which are the averages of all defect-free fabric images’ features. At last, according to this compared distance, the patch is classified as defective or non-defective. The experimental results on our own database and the TILDA database reveal that our features are more robust and the proposed algorithm can detect defects on twill, plain, gingham and striped fabric effectively.