D4Net: De-deformation defect detection network for non-rigid products with large patterns

Abstract Defect detection is one of the key steps in quality control in manufacturing industries. Distinguishing unwanted defects and acceptable deformation for non-rigid products with large patterns is a challenging yet rarely researched task. In this work, a de-deformation defect detection network (D4Net) is proposed to detect defects of a non-rigid product with deformation in a given image and its corresponding reference image. The proposed method focuses on differences between high-level semantic features extracted from the deep neural network to emphasize the region of possible defects. In training, a marginal loss is proposed to improve the separability between defects and deformation in images with large patterns. Experimental results show that the D4Net yields the best performances of 96.9 % accuracy and 91.7 % F-measure in a real industrial dataset consisting of 67K images of lace fabric with large patterns from a worldwide top-10 lace fabric manufacturing company. This validates the effectiveness of the proposed method in industrial applications.

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