A probabilistic approach for defect detection based on saliency mechanisms

The use of saliency mechanisms for defect detection is discussed in this work. We consider defects on regular surfaces as conspicuous areas that catch the attention of the surveyors. Following this approach, we propose the use of the Bayesian framework SUN, devised to provide saliency information based on natural statistics, to combine information about the visual appearance of the surface under inspection, to finally indicate where the defects (if any) are located. The visual information is suggested to be based on features commonly used to predict human eye fixations: contrast and symmetry. We demonstrate that these two features provide a description of the surface that can be used to indicate whether it is defective or not. Our approach is assessed using a publicly available image dataset containing a variety of surfaces with defective areas. The performance of the defect detector is evaluated through cross-validation and successful results are obtained.