Mean Shift-Based Defect Detection in Multicrystalline Solar Wafer Surfaces
This paper presents an automated visual inspection scheme for multicrystalline solar wafers using the mean-shift technique. The surface quality of a solar wafer critically determines the conversion efficiency of the solar cell. A multicrystalline solar wafer contains random grain structures and results in a heterogeneous texture in the sensed image, which makes the defect detection task extremely difficult. Mean-shift technique that moves each data point to the mode of the data based on a kernel density estimator is applied for detecting subtle defects in a complicated background. Since the grain edges enclosed in a small spatial window in the solar wafer show more consistent edge directions and a defect region presents a high variation of edge directions, the entropy of gradient directions in a small neighborhood window is initially calculated to convert the gray-level image into an entropy image. The mean-shift smoothing procedure is then performed on the entropy image to remove noise and defect-free grain edges. The preserved edge points in the filtered image can then be easily identified as defective ones by a simple adaptive threshold. Experimental results have shown the proposed method performs effectively for detecting fingerprint and contamination defects in solar wafer surfaces.
defect detection multicrystalline solar wafer machine vision mean shift surface inspection