Early detection of operational microdefects of protective coatings and corrosion damage is important for reliable and long-term operation of materials and structures. Corrosion damage in the early stages is almost impossible to detect by visual testing methods, given the microscopic size of such damage. Laboratory microscopes are unsuitable for field conditions. Flash meters provide an estimate of surface roughness, but do not provide a surface concentration of microdefects, especially when these defects are heterogeneous or polydisperse. A compact prismatic sensor of the angular characteristic of diffuse light reflection is proposed for the detection of microdefects and express diagnostics of surface quality. To increase the informativity of the sensor signal during measurements, the useful signal was calculated as the difference between the base values of the diffuse reflected signal for a surface without defects and the measured values for a surface with defects. The dependence of the signal distribution on the photoline on the geometry of the sensor was taken into account. The article considers the problem of classification by the average size of surface microdefects collected in submillimeter clusters, randomly placed on the surface of materials. The use of numerical methods for solving inverse problems requires large computing power and time, which is not always acceptable for field measurements. The dependence of solution stability on signal measurement errors and random locations of clusters of microdefects on the illuminated area of the investigated surface is important. An effective set of informative signal features is selected. The study explored the possibility of using alternative sets of informative parameters derived from the base set. It was proposed to determine the size of the defects based on the smoothness criteria of the contour signal of the diffuse light reflection sensor. To evaluate the characteristics of the spatial configuration of defects, the positions of the peaks of the signal distribution along the photoline, determined with subpixel accuracy, were used. The classification is made for the selected information features of the signals of the diffuse light reflection sensor. In particular, the number of local signal extrema together with the subpixel position of the global signal maximum produces well-separated clusters, even for randomly located defect aggregations.

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