# Monocular Vision-Based Depth Estimation of Forward-Looking Scenes for Mobile Platforms
## 3 Experiment
### Performance Evaluation for UAVs  
The objective of this paper is to propose a methodology for addressing the challenge of forward-looking depth estimation in IMPs, including UAVs and AGVs. The experiment described in Section 3.1 was conducted using AGVs. Consequently, the experiments in this section will evaluate the depth estimation performance of the proposed method in UAV application scenarios. The image sequences utilized in this section were captured using _AirSim_, an open-source simulation platform developed by Microsoft. This platform is capable of simultaneously acquiring visible video sequences as input data and corresponding high-resolution depth information as ground truth. The experimental scenarios encompass two distinct environments: the snowy mountain scenario and the urban neighborhood scenario, with the aim of verifying the usability and robustness of the proposed method across different settings. **Dataset**: The UAV flight forward-looking simulation dataset is divided into two groups based on different scenarios: (1) The sub-dataset from the snowy mountain scenario, referred to as _AirSim_Mountain_, contains 3986 images for training and 1453 images for testing. _AirSim_Mountain_ features a diverse array of environmental elements, such as trees, mountains, and rivers, which contribute to its highly complex terrain characteristics. This complexity presents various challenges for the proposed method, including issues related to occlusion, height variations, and terrain irregularities. (2) In contrast to _AirSim_Mountain_, the UAVs in the _AirSim_CityRoad_ sub-dataset primarily navigate along city roads, exhibiting relatively simple motion states that include only straight-line flight and turning. This sub-dataset comprises 3174 images for training and 1206 images for testing. Some samples from the UAV flight dataset are illustrated in Figure 8.  
Figure 7: The results of qualitative comparison based on the _KITTI_ dataset. (**a**) Test images. (**b**) The proposed method. (**c**) _HR-Depth_. (**d**) _Monodepth2_. **Implementation Details**: Similar to the previous experiment, the experimental platform utilizes Ubuntu 20.04.5 as the operating system, while the neural network models are constructed using the PyTorch 1.7 open-source framework. The camera intrinsic matrix \(\mathbf{K}\) of the \(AirSim\) is presented in Equation (12). During the training process, we also employ the Adam optimizer for gradient descent optimization. The model is trained for 20 epochs with a batch size of 8. The learning rate is initially set to \(10^{-4}\) for the first 15 epochs and is subsequently adjusted to \(10^{-5}\) for the remaining 5 epochs. \[\mathbf{K}= \begin{bmatrix}320&0&320\\ 0&359.185&240\\ 0&0&1\end{bmatrix}  \tag{12}\]  
**Quantitative Comparison and Evaluation**: Since the \(AirSim\_Mountain\) and \(AirSim\_CityRoad\) sub-datasets were generated with pixel-level depth labeling, the performance of depth estimation methods on the \(AirSim\) dataset can be evaluated quantitatively by directly comparing the predicted depth of each pixel with the true depth. As shown in Table 3, all assessment criteria for the proposed method significantly outperform those of the other two methods. The prediction accuracy also demonstrates a high level of performance; for instance, using \(Abs\_Rel\) and \(thr\)\(Acc\) (where \(thr\) = 1.25) as examples, the proposed method reduces the error by a maximum of 7.9% and improves the accuracy by a maximum of 1.3% compared to _Monodepth2_, which serves as the baseline method. Therefore, the quantitative comparison results clearly demonstrate the significant performance advantage of our method in the depth estimation task, as well as the validity of the improvements and optimizations made to the network architecture in the proposed approach.