## 5 Conclusions  
A new software-hardware-based method for evaluating the effect of illumination on the efficacy of recognising metal strip defects has been created, which includes the specifically developed laboratory setup and artificial intelligence algorithms. The proposed method, combined with digital imaging and computer techniques, allows for testing the parameters of technical control systems intended to detect metal rolling defects and evaluate their performance. The method is based on the statistical estimation of quantitative defect parameters. Defect area and orientation were used as descriptive quantitative parameters for the investigated surface. The influence of variable surface illumination on the variability of the quantitative parameters (area and orientation) of recognised damage was investigated for the first time using the proposed method. The optimal lighting range for two light sources (LED unit and incandescent lamp) was found. A specimen to which variable illumination from two different light sources was applied was used as an example. This was done to illustrate that changing the type of illumination can significantly affect the recognition results. Regardless of the light source, changing the average illumination in the lower range (which may differ slightly for various light sources) greatly impacts the recognition result. When varied in this range, illumination can significantly affect the result of damage recognition. At the same time, a further increase in illumination does not significantly affect the quantitative parameters of the recognition results but slightly increases their scatter. The best result for investigated steel sheet defects (\(DSC=0.93\)) was obtained at illuminance \(\sim\)200 lx. Illuminance less than 150 lx does not allow the recognition of most defects, whereas illuminance larger than 250 lx increases the number of small objects falsely recognised as defects. The proposed method allows us to estimate the variance in quantitative parameters depending on illuminance and choose the optimal level of illuminance to detect defects with the highest accuracy. This will aid in adopting the defect recognition platform in actual industrial conditions and enhancing the quality of the investigated equipment. Supervision and Data validation: I.K. and P.M.; Data collection, Information analysis, Writing, and Editing: P.M., I.K., Y.O., V.M., O.S., D.B., H.K. and R.M. All authors have read and agreed to the published version of the manuscript. Figure 16: Dependence of the \(DSC\) metric on the average surface illumination for the LED light source. Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. Conflicts of Interest: The authors declare no conflicts of interest. References  
* [PERSON] and [PERSON] (2000) [PERSON]; [PERSON] [PERSON] Real time DSP based identification of surface defects using content-based imaging technique. In Proceedings of the IEEE International Conference on Industrial Technology 2000 (IEEE Cat. No.00 TH8482), Goa, India, 19-22 January 2000; Volume 1, pp. 113-118. [CrossRef]
* [PERSON] et al. (2017) [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] _Sustainable Renovation within Metallurgical Production_; RAM: Ludenscheid, Germany, 2017; 215p. * [PERSON] et al. (2023) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] Light Source Modules for Defect Detection on Highly Reflective Metallic Surfaces. _Metals_**2023**, 13, 861. [CrossRef]
* [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON] Improvements for the Recognition Rate of Surface Defects of Aluminum Sheets. In _Light Metals 2019_; The Minerals, Metals & Materials Series; [PERSON]; Springer: Cham, Switzerland, 2019. [CrossRef]
* [PERSON] et al.