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The article formulates and proves a new approach to assessing the quality of lighting based on the luminance spatial-angular distribution (LSAD). Several aspects of assessing the quality of lighting are described and the main one is selected for further research. Visibility is considered as the first step to the formulation of the quality criterion. The validity of the new approach is proved in laboratory conditions on the example of carrying out a standard visual task and in real conditions in the Moscow metro.

The article proposes the classification of lenses with different symmetrical beam angles and offers a scale as a spot-light’s palette. A collection of spotlight’s images was created and classified according to the proposed scale. The analysis of 788 pcs of existing lenses and reflectors with different LEDs and COBs carried out, and the dependence of the axial light intensity from beam angle was obtained. A transfer training of new deep convolutional neural network (CNN) based on the pre-trained GoogleNet was performed using this collection. GradCAM analysis showed that the trained network correctly identifies the features of objects. This work allows us to classify arbitrary spotlights with an accuracy of about 80 %. Thus, light designer can determine the class of spotlight and corresponding type of lens with its technical parameters using this new model based on CCN.