Abstract— A technique and an algorithm of digital surface image processing are proposed to increase the validity of real-time detection of small size defects. The algorithm is implemented in the MATLAB programming environment. The technique is based on segmentation of the high-frequency component of surface texture because small size defects are especially pronounced in this component. The high-frequency component, in particular roughness, is extracted by means of wavelet transform for frequency components separation and homomorphic filtration for compensation of low-frequency distortion caused by nonuniform illumination of test surface. Segmentation of the high-frequency texture component consists in formation of a binary image using the texture descriptors derived from the gray-level co-occurrence matrix as the segmentation threshold. The proposed technique and algorithm are approved in applications to defect detection for a simulated surface, for real ground surface of hardened steel, and for surfaces of carbon fiber reinforced plastic composite. Extraction efficiency of the high-frequency component of surface texture is shown. It is found that texture descriptors, “contrast’ and “energy,” can be applied as segmentation thresholds for defect extraction/determination on the ground (anisotropic) surface while segmentation of an image of a plastic composite (isotropic) surface is effective just with “energy” as a threshold. The proposed technique can be applied for simultaneously real-time monitoring the surface texture and detecting the small size defect in machine vision systems during production and operation of tribosystems.