# UAV-Based Airport Lights Inspection Without GNSS Positioning Support
## 3 Materials and Methods  
The identification of airport light properties specifically means matching the observed lights to documented lights on the runway and comparing the property data. The task poses the problem of assessing the light parameters and determining the light positions. The result of an inspection provides information for maintenance crews for correcting the operation of a light or carrying out a replacement. Employing unmanned aerial vehicles for inspections enables the efficient scanning of airfield runways without the excessive disruption of airport operations [30]. UAV flights can be carried out at night when there is much less air traffic. Night flights create favourable conditions for assessing light luminosity as sunlight interference is eliminated. The basic light parameters of colour and luminosity are the subjects of evaluation. Cameras with appropriate sensors for night vision can be used. The determination of light positions requires some means of assessing the space coordinates of the camera attached to the moving UAV. This operation is usually performed using GNSS-based methods with augmentation. Augmentation involves the use of additional sources of information such as reference stations and complicates the process of position calculations. SfM enables the estimation of space coordinates from image sequences. When the camera moves, lights on the image change positions by different amounts depending on their distance from the camera. The size of the light position changes maps depth information, which is used to generate a 3D layout of the lights on the airport runway. The correspondence between the images and the reconstructed 3D objects needs to be determined. This correspondence refers to the task of determining which parts of one image correspond to which parts of another image; in this case, the parts are lights described by features. To find correspondence between images, features are tracked from one image to the next. Successful feature extraction methods such as the Scale-Invariant Feature Transform (SIFT) [31] are applied for describing object properties. In the case of the observed airport lights, a much less computationally demanding approach is employed. UAV flights are carried out late in the day, and the ambient light is substantially less intense. Lights become the outstanding elements of the registered images, and their detection can be realized through elementary processing involving grey-scale morphological operations. Estimating changes in the positions of lights in a sequence of images requires reliable light tracking. The first step is to extract and mark the lights on the image. Next, the marked lights are tracked in the following images. Depending on the image resolution, height of the flight, and speed of the UAV, the lights can move tens of pixels on the image. The careful setup of the filming conditions is necessary for reliable tracking. Large movements impair the calculation of correspondences. Small movements may not be detected as the UAV flight is limited in time due to battery capacity and the permissible time of shutting down the runway for air flights. Important for the correct calculation of lights coordinates is finding the scale of projection and thus the relative positions of the UAV and observed airport lights. Airport geodesic data provide reference information about the positions of the lights, and this is used for scaling and calculating the space coordinates of the flying UAV for tracking. Figure 1 presents an image sample recorded during a UAV inspection flight. Only the centre part of the image is processed, and detected lights are marked with blue rectangles. The size of the image segment used for processing depends on the height of the UAV flight, view angle of the camera mounted on the UAV, and on the camera sensor resolution.