Accumulated and aggregated shifting of intensity for defect detection on micro 3D textured surfaces
作者：
Highlights：
• This paper presents an unsupervised and training-free method for defect detection on micro 3D textured surfaces, which is difficult due to low-contrast and an unclear boundary between defect and irregular textured defect-free region.
• An accumulated and aggregated shifting of intensity (AASI) procedure is proposed to iteratively enhance defects, which solves the defect detection problem under a probabilistic saliency framework.
• A statistical distribution fitting rule is then proposed for pixel-level classification, which avoids the problem of collecting and labeling a large amount of data.
• Experiments on real-world industrial surfaces demonstrate the feasibility and robustness of our approach.
摘要
•This paper presents an unsupervised and training-free method for defect detection on micro 3D textured surfaces, which is difficult due to low-contrast and an unclear boundary between defect and irregular textured defect-free region.•An accumulated and aggregated shifting of intensity (AASI) procedure is proposed to iteratively enhance defects, which solves the defect detection problem under a probabilistic saliency framework.•A statistical distribution fitting rule is then proposed for pixel-level classification, which avoids the problem of collecting and labeling a large amount of data.•Experiments on real-world industrial surfaces demonstrate the feasibility and robustness of our approach.
论文关键词：Defect detection,Accumulated and aggregated shifting of intensity (AASI) procedure,Saliency description,Illumination invariance
论文评审过程：Received 23 February 2019, Revised 13 August 2019, Accepted 13 September 2019, Available online 13 September 2019, Version of Record 19 September 2019.
论文官网地址：https://doi.org/10.1016/j.patcog.2019.107057
