# Monocular Vision-Based Depth Estimation of Forward-Looking Scenes for Mobile Platforms
## 3 Experiment
### Performance Evaluation for UAVs
**Qualitative Comparison and Evaluation:** For the test results of the samples from the \(AirSim\_Mountain\) dataset shown in Figure 9a, when the UAV is flying in a mountainous area, the proposed method can accurately distinguish the relative distances and proximity of obstacle targets in its path, such as trees, peaks, and rocks. Additionally, compared to the other two methods, our approach provides a more detailed analysis of contours and edge regions. Furthermore, due to the complexity of mountainous environments, texture information often introduces visual interference, such as the varying shapes of snow bands and rivers, which can easily lead to errors in judgment and negatively impact the overall accuracy of depth estimation. However, the method presented in this paper can effectively  
Figure 8: Samples from the \(Airsim\) datasets. (**a**) \(AirSim\_Mountain\). (**b**) \(AirSim\_CityRoad\). address these challenges by focusing solely on the spatial relationships within the scene while disregarding the adverse effects of texture information, thereby yielding more precise depth prediction results. It is important to note that the distant targets in the depth map have blended into the background, making it difficult to estimate their depth accurately. For the _AirSim_CityRoad_ dataset, using four samples from Figure 9b as examples, the depth estimation method proposed in this paper demonstrates a comprehensive understanding of road structure and analyzes the geometric features of intersections. It exhibits strong depth perception capabilities in urban road scenes. Furthermore, our method accurately captures and interprets the environmental features on both sides of the road, particularly objects such as trees, lampposts, and road signs. It effectively represents the spatial locations and distances of these targets in depth maps, thereby addressing the visual challenges presented by complex road scenes. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Test Sample** & **Method** & _Abs\_Rel_ & _Sq\_Rel_ & _RMSE\_Log_ & _Thr Acc (thr = 1.25)_ & _Thr Acc (thr = 1.25\({}^{\circ}\))_ & _Thr Acc (thr = 1.25\({}^{\circ}\))_ \\ \hline \multirow{2}{*}{_AirSim_Mountain_} & _Proposed_ & **0.211** & **1.241** & **5.493** & **0.321** & **0.783** & **0.943** & **0.982** \\  & _HR-Depth_ & 0.218 & 1.392 & 5.864 & 0.395 & 0.771 & 0.940 & 0.979 \\  & _Monodepth2_ & 0.229 & 1.503 & 6.104 & 0.461 & 0.773 & 0.924 & 0.978 \\ \hline \multirow{2}{*}{_AirSim_CityRoad_} & _Proposed_ & **0.242** & **1.364** & **5.790** & **0.355** & **0.779** & **0.942** & **0.980** \\  & _HR-Depth_ & 0.253 & 1.472 & 5.941 & 0.432 & 0.776 & 0.941 & 0.978 \\  & _Monodepth2_ & 0.256 & 1.577 & 6.077 & 0.515 & 0.772 & 0.939 & 0.977 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The results of quantitative comparison based on the _AirSim_ simulation dataset. (The best result in each column in each sub-dataset is shown in bold). Figure 9: _Cont._  
Under the same experimental conditions, we have evaluated the inference speed of the proposed method using images at two resolutions. For images sized 384 \(\times\) 192 pixels and 640 \(\times\) 320 pixels, the inference speeds were 56.3 frames per second (_FPS_) and 40.8 _FPS_, respectively. For real-time applications, the proposed method achieves a reasoning speed exceeding 40 _FPS_ for images sized 640 \(\times\) 320 pixels. However, the majority of civil IMPs currently in use support a video frame rate of approximately 30 _FPS_. Consequently, our method can achieve real-time inference for depth estimation.