## 4 Experiments and Results  
The aircraft engine detection tests were conducted by distinguishing between the target aircraft and the time of day. The target aircraft were broadly categorized into two types, and as with the previously trained dataset, tests were performed on the B787 and A330 models. These tests were conducted both in the evening and during the day, as the amount of light reflected on each aircraft's exterior varied depending on the time of day. This variation led to relative distortions in the data collected from areas where a significant amount of sunlight was reflected. As shown in Table 1, the variance error in the data caused by these distortions showed an error range of more than 0.5 cm, deviating from the specifications provided by the basic system. To address this, various test scenarios were introduced to differentiate between the technical limitations of the sensor system and the algorithm under different conditions. Although the B787 and A330 models were from different airlines, the engine areas of both aircraft were white, meaning there was no difference due to the basic engine color. The first test was conducted from 10:00 to 11:00 in the winter, and the second test was performed from 21:00 to 23:00 during the same season. Figure 8 shows a photo of the aircraft fuselage taken from the top of the boarding bridge during the test, with light and shadows appearing as shown in the image. Additionally, as shown in Figure 9, the result data include the measured straight-line distance from the center of the rotating part of the boarding bridge to the nearest point of the aircraft engine. In the first experiment, as shown in Figure 9, we conducted a test for aircraft engine data segmentation and distance measurements by moving the jetbridge to approximately 3.5 m in front of the aircraft engine. The actual test was performed 10 times, and with the previously collected dataset, it was repeated 50 times. The data sampling rate was set to 10 Hz. As a result, the difference in the outcomes between the B787 and A330 aircraft was not significant. For the B787, the segmentation results showed an accuracy of over 81% when the distance from the jetbridge was between 10 m and 4 m, although the accuracy dropped to 88% when the distance was between 4 m and 3.5 m. Consequently, the mIoU for the B787 in the first experiment was recorded at 0.805. For the A330, there was no significant difference across all distances, and the mIoU was recorded at 0.82. Thus, the semantic segmentation mIoU for the two aircraft types in the first test scenario was 0.8125. Figure 8: Center of the jetbridge and distance between the jetbridge and engine. Figure 9: Result of the segmentation with the PointNet model (daytime). Jetbridge(blue box). Additionally, we measured the distance error between the segmented data classes and the jetbridge. The measurements were compared at distances of 10 m, 5 m, and 3 m using a high-precision radar distance measuring sensor from SICK. The purpose of measuring the distance error was to compare the performance of the semantic segmentation method under external environmental changes, such as external vibrations affecting the sensor system or interference in the sensor data field of view during scenarios where the target object is stationary and the sensor system is moving. As shown in Figure 10, changes in cabin heading angles affect the measured distance data between the aircraft engine and the jetbridge. When the driving trajectory is straight, the error between the reference data and the measured data is close to zero. However, in sections where the jetbridge's heading changes, vibrations affecting the sensor system result in data errors of up to 0.53 m.  
In fact, as shown in the upper graph of Figure 10, the high frequency of vibrations caused by angle changes significantly affects the sensors. The correlation between the two graphs indicates that the greater the distance between the aircraft engine and the jetbridge, the larger the measurement data error due to the same system vibrations.