# Monocular Vision-Based Depth Estimation of Forward-Looking Scenes for Mobile Platforms
## 3 Experiment
### Performance Evaluation for AGVs  
**Dataset**: The _KITTI_ dataset is widely recognized as the most extensively utilized publicly available dataset for depth estimation. Since the videos in this dataset are captured by AGVs, we use it to test and evaluate the proposed method applied to AGVs. In this experiment, we adopt the data-splitting method proposed by [PERSON] et al. [19] and manually exclude the static frames, following the approach of [PERSON] et al. [29]. Ultimately, the training set consists of 39,810 images, the validation set contains 4424 images, and the test set comprises 697 images. **Implementation Details**: The experimental platform operates on Ubuntu 20.04.5, while the neural network models are built using the PyTorch 1.7 open-source framework. The camera intrinsic matrix \(\mathbf{K}\) is presented in Equation (11), derived from the intrinsic parameters of the acquisition used in the _KITTI_ dataset. The workstation is equipped with an RTX 3090 Ti graphics card, and during the training process, we employ the Adam optimizer for gradient descent optimization. The model is trained for 20 epochs, with a batch size of 8 determined by the workstation's performance. The learning rate is set to \(10^{-4}\) for the first 15 epochs and is subsequently adjusted to \(10^{-5}\) for the remaining 5 epochs. \[\mathrm{K}= \begin{bmatrix}718.8560&0&607.1928\\ 0&718.8560&185.2157\\ 0&0&1\end{bmatrix}  \tag{11}\]  
**Quantitative Comparison and Evaluation**: The proposed method and two other baseline methods, _HR-Depth_ and _Monodepth2_, are compared using the same test sets, and the results of the comparative experiment are presented in Table 2. It can be seen that the proposed method demonstrated superior performance on the test set due to structural \begin{table}
\begin{tabular}{c c} \hline \hline
**Evaluation Index** & **Formula** \\ \hline Absolute relative error & \(\dfrac{1}{N}\sum\limits_{i=1}^{N}\dfrac{\left|d_{i}-d_{i}^{*}\right|}{d_{i}^{* }}\) \\ \hline Square relative error & \(\dfrac{1}{N}\sum\limits_{i=1}^{N}\dfrac{\left|d_{i}-d_{i}^{*}\right|^{2}}{d_{i} ^{*}}\) \\ \hline Root mean square error & \(\sqrt{\dfrac{1}{N}\sum\limits_{i=1}^{N}\left|d_{i}-d_{i}^{*}\right|^{2}}\) \\ \hline Root mean square logarithmic error & \(\sqrt{\dfrac{1}{N}\sum\limits_{i=1}^{N}\left|\log(d_{i})-\log(d_{i}^{*}) \right|^{2}}\) \\ \hline Threshold & \(\max\left(\dfrac{d_{i}}{d_{i}^{*}},\dfrac{d_{i}^{*}}{d_{i}^{*}}\right)=\delta< \mathit{thr}\) \\ \hline _thr Acc_ & _thr_ = 1.25, 1.25\({}^{2}\), 1.25\({}^{3}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Quantitative evaluation metrics. upgrades and the addition of ECANet, as evidenced by most indexes outperforming the two other methods. To evaluate the impact of the first and second contributions on depth estimation performance, we conducted ablation experiments on the _KITTI_ dataset. As previously mentioned, the contributions of this paper consist of two main components: the design of feature aggregation nodes (FANs) and the incorporation of ECANet in the decoder. Consequently, we tested four algorithms in the ablation experiments: _Baseline (BL)_, _Baseline_ with integrated feature aggregation nodes (_BL+FAN_), Baseline+FAN with the introduction of SENet in the decoder (_Baseline+FAN+SE_), and our proposed approach (_Baseline+FAN+ECA_). The results of the ablation experiments are presented in Figure 6. Based on the changes observed in the four evaluation metrics presented in Figure 6, we can draw the following conclusions: (1) Integrating feature aggregation nodes into the baseline model improves depth estimation performance to some extent; (2) the algorithm's performance can be significantly enhanced by utilizing ECANet instead of SENet within the Baseline+FAN framework. **Qualitative Comparison and Evaluation**: The depth maps of Figure 7 utilize distinct color variations to delineate the relative spatial relationships within the scene. Lighter hues indicate proximity, while darker shades signify greater distance.