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Aiming to solve the problems of identifying the over-brightness and over-dark parts in the reflected light wear debris image, this paper proposed a new OLVF (on-line visual ferrgraph) reflected light wear debris segmentation algorithm. If the wear debris image is bright and dark, the morphological black-hat operation is used to eliminate these interferences. Then the H-minima transform is applied to corrected image local extreme value interference and the threshold is obtain through Otsu algorithm. The watershed algorithm is adopted to separate over-bright and over-dark wear debris. Eventually the morphological dilate corrosion of opening and closing reconstruction is applied to filled holes and connected wear debris profile. On the basis of the above-mentioned method, the OLVF reflected light wear debris are accurate shape and continuous profile in the final result. Compared with other wear debris divide algorithms, the proposed method effectively suppresses the influence of reflected light, and better retain the information of wear debris image. At the same time, the signal-to-noise ratio is 12dB higher than others at the maximum, and the root mean square error is reduced by 0.051 at the maximum.

A Segmentation Algorithm of Wear Debris Reflected image Based on Watershed and H-minima Transform for On-Line Visual Ferrograph Analysis