精密工学会学術講演会講演論文集
2018 JSPE Autumn Conference
会議情報

Accumulated Aggregation Shifting and Its Application to Robust Defect Detection on SHIBO Surfaces in Real Industry
*YAN YapingXIANG ShengASANO HirokazuKANEKO Shun’ichi
著者情報
キーワード: defect detection
会議録・要旨集 フリー

p. 636-637

詳細
抄録

Detecting defects on 3D textured low-contrast surfaces plays an important role in product quality control. However, because of affects from the uneven distributions of material, irregular textures, and unclear boundary between defect and background, this is a very challenging problem. In this paper, we propose an unsupervised defect detection method guided by saliency. Firstly, two features, named local-global intensity difference and local intensity aggregation, are proposed to measure saliency of each pixel. These two features are further utilized to construct an accumulated aggregation shifting (AAS) model, which iteratively shifts brightness of pixels based on their visual saliency, i.e. defective probability. And then, the output sequence of AAS at different iterations can be formalized as linear distribution or exponential distribution through statistical analysis. Finally, by utilizing the risk minimization method, we theoretically determine a reasonable threshold to classify all pixels as defective ones or defect-free ones. Experiments on a real industrial image dataset demonstrate the effectiveness of our approach.

著者関連情報
© 2018 The Japan Society for Precision Engineering
前の記事 次の記事
feedback
Top