## 4 Discussion  
As is seen from the results obtained, the area of the sections recognised as damage increases with an increase in the illuminance level applied to the surface of interest. This is because minor scratches (and other morphological elements of the surface) become more visible. A transition from a lower to a higher light is especially noticeable (from 37 to 64 \(\mathrm{lx}\) for the LED source or from 36 to 70 \(\mathrm{lx}\) for the incandescent lamp). In the former case, the vertical scratches can hardly be seen in the image, while with the average light of \(-64\) \(\mathrm{lx}\), some become recognisable. At 159 \(\mathrm{lx}\), the neural network recognises most of them (with the LED source). When increased above 150 \(\mathrm{lx}\), the light level adds nothing to detecting new scratches (except, perhaps, very small ones). At the same time, it makes the objects of damage somewhat larger. Therefore, we can conclude that the total area of damage in the image increases with an increase in the light level. We note that the neural network is very sensitive even in low light, especially for images obtained using the LED source; most pronounced damage is well-recognised at the average light level of 64 \(\mathrm{lx}\), even though the camera adds darkness to the images, making damage practically invisible (Figure 11a, third image). However, the damage is no longer recognisable at 37 \(\mathrm{lx}\) on average (Figure 11a, the last image). In this case, pixel intensity varies from 1 to 5 in the zone with unrecognisable damage (see the initial photo). In other words, we can state that with 37 \(\mathrm{lx}\), the input image obtained is not informative enough to conclude the presence of the objects of interest. Therefore, the neural network model is good at recognising the objects of interest in all the images where at least a small gradient is preserved between the damage pixels and the background. Low-light images have another feature; small objects become invisible in them. Therefore, low light should be used when the most pronounced damage needs to be recognised. This will provide for much lower noise in the image caused by an increased detail of surface artefacts that may not belong to damage (in high light). The area of damage is one of the most essential and apparent parameters characterising the surface. Figure 13 shows diagrams of the total areas recognised as damage using the two light sources considered. There is a significant difference in the total areas recognised as damage. This is because thin vertical scratches are practically not recognised when using the incandescent lamp (Figure 12b). When the illumination generated by the LED source varies from 35 to 160 \(\mathrm{lx}\), the total area recognised as damage noticeably increases from 5000 pixels to 100,000 pixels (Figure 13a). A further increase in illumination does not entail a significant increase in this parameter (its values range from 100 to 125 thousand pixels). That is, the neural network has detected all the damage it was trained for. Further increase in the light level will not  
Figure 13: Dependence of the area recognised as damage on the illumination produced by the LED source (**a**) and the incandescent lamp (**b**). add any new details that could be identified as damage. A similar pattern is observed for the incandescent lamp (Figure 13b). With the light level of 108 lx, the area of damage recognised increases only slightly. An exception is a very high light of 270 lx, at which some vertical scratches, which were not detected before, are starting to be recognised. The scatter of damage areas recognised in the image under the LED source is shown in Figure 14a,b. On the scatter plot (Figure 14a), each marker corresponds to a particular fragment of damage. The graph allows us to conclude that most fragments have areas up to 15 thousand pixels. Areas bigger than 20 thousand pixels contain individual fragments. The box plot (Figure 14b) contains percentiles that characterise the scatter of areas.