Accumulated Aggregation Shifting Based on Feature Enhancement for Defect Detection on 3D Textured Low-Contrast Surfaces

摘要

Detecting defects on 3D textured low-contrast surfaces plays an important role in product quality control. However, because of the affects from uneven distributions of materials, irregular textures, and unclear boundaries between defects and background, this is still a challenging problem. In this paper, a saliency-guided defect detection method, named accumulated aggregation shifting (AAS) model, is proposed to iteratively shift brightness of pixels based on their defective probability. And then, the output sequences 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. This method models defect detection problem under a probabilistic framework. And only a handful of samples are needed for parameter optimization. Experiments on a real-world image dataset for an industrial surface defect detection task demonstrate the effectiveness of our approach.