# UAV-Based Airport Lights Inspection Without GNSS Positioning Support
## 4 Results  
The task of measuring the angles of colour changes in PAPI lights was the focus of this study. The proposed processing procedure was implemented using C/C++ (v.20) and OpenCV (v.4.8.0) libraries. The UAV was equipped with an onboard processing unit aided by a 256 core CUDA-based module. The processing resources proved adequate for obtaining measuring results during the UAV flight. Tests with a much less powerful processing unit (Raspberry PI4), for use with other UAVs, also proved successful. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Lights** & **Processed Area** & **Width [mm]** & **Height [mm]** \\ \hline ALS & 3840 \(\times\) 557 & 13–53 & 83–2043 \\ runway & 1920 \(\times\) 278 & 26–106 & 167–4065 \\ PAPI & 3840 \(\times\) 308 & 7–14 & 233–927 \\
1920 \(\times\) 154 & 14–28 & 467–1849 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Mapped pixels when the UAV flies 10 m above the airport plane. The measuring procedure complies with ICAO regulations Doc 9157 part 4. 8.3.43-8.3.47 [2]. The course of light changes is recorded during UAV ascent at a distance of 300 m from the set of PAPI lights. Transition zones from red to white were determined, and the colour switch angles were calculated. Figure 6 shows the sequence of colour changes during the ascent of the UAV. The change in colours happens gradually, and the exact transition moment during the flight is not easy to determine. The change in colours is additionally distorted by the overexposure at the centre of the light. This effect arises when the camera is used in low light. The effect of overexposure can be reduced by changing the sensitivity of the camera sensor. Lowering sensitivity reduces the size of the image of the light, which by consequence may lead to errors in determining its colour. High sensitivities build up noise corrupting the process of extracting contours. The reliable automatic detection of colour change is hindered by colour noise when the camera operates in the RGB colour space. Numerous films recorded during the UAV ascent indicate that the colour transition zones are blurred, which gives ambiguous detections. The conversion of the image content to other colour spaces was investigated [39]. The YUV colour space proved to be the most robust to noise. The chroma component V most distinctly differentiates white and red; it is used to detect the colour change. The next step is to analyze the view of the lights at appropriate increments of the UAV ascent in order to obtain the required resolution of determining the transition angle. The increment is determined by the speed of the UAV and the filming speed of the camera. The camera works at 30 fps, and filming 300 m from the objects gives a view angle change of 1\({}^{\prime}\) for every 8 cm of ascent. Keeping the ascend speed lower than 2.6 m/s results in an angle resolution smaller than 1\({}^{\prime}\). Crucial for the calculation of view angles is the precision of determining the position of the camera. Perspective-n-Point pose computation is sensitive to the number of points used in calculations. Tests showed that at least 15 reference points were required to achieve a camera position error enabling the calculation of angles with a smaller error than 5\({}^{\prime}\). Figure 7 presents an exemplary layout of the reference points. Lights 1-4 are the PAPI lights, whereas lights 5-15 are the neighbouring runway lights. Figure 6: PAPI lights: (**a**) all red, (**b**) 1 white, (**c**) 2 white, (**d**) 3 white, (**e**) all white. All flights were carried out with the camera horizontal FOV set to 58\({}^{\circ}\) and the lens focal length set to 6.83 mm in order to view at least 15 reference lights and film at 30 fps at a 3840 \(\times\) 2160 resolution, which gives an error rate of calculating the transition angle in the range of 0.15-0.32%. Sensitivity is set automatically by the camera and can change in the range of 100-25,600 ISO; this enhances the exposure of the lights.