A Dark -Field Detection Algorithm to Detect Surface Contamination in Large-Aperture Reflectors

In this study, the dark-field detection algorithm, which is suitable for the detection of contaminants, is investigated in accordance with the imaging characteristics of the surface contaminants of the large-aperture reflector. In this algorithm, the autofocus algorithm is considered during the image acquisition process, whereas the distortion correction and pollutant extraction algorithms arc considered during the image processing process. Further, the Tenengrad function is selected to evaluate the sharpness during the autofocus process, and a coarseprecision peak search strategy is proposed to improve the focusing accuracy. Based on the distortion model, the distortion correction algorithm calculates the distortion model coefficients in accordance with the projective transformation properties of the calibration plate corner points and implements image distortion correction. The root mean square error of the correction result is 3. 3092 pixel. In the contaminant extraction algorithm, the top-hat transform is employed to eliminate the image background, and the Laplacian weighted adaptive binarization algorithm is used to extract contaminants from the background-removed image. The algorithm is effective for the image with small-sized pollutants in case of uneven illumination. The error in the amount of detected contaminants is 7%. The detection accuracy of the proposed method is better than those of the global threshold algorithm and the mean operator weighted adaptive binarization algorithm. Furthermore, the detection algorithm can provide technical support to evaluate the clean state of the reflector.