# Autonomous Environment Generator for UAV-Based Simulation
## 5 Experimental Results and Discussion  
This section aims to showcase a complete sample simulation and compare its results against those of the platforms described in the related works. We used six different features as indicators for this comparison, as highlighted in Table 3.  
The entire image-to-simulation process is demonstrated in the following figures. Figure 13(a) represents the unaltered image of a Las Vegas city block. Figure 13(b) demonstrates our Mask R-CNN being applied to that same image. Figure 13(c) shows the YOLO algorithm on the same image, as well. And, finally, Figure 14(a)-c show the complete Gazebo result. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metrics** & **ImageNet** & **ImageNet (Fine-Tuned)** & **COCO** & **COCO (Fine-Tuned)** \\ \hline Training Accuracy & 56.40\% & 79.19\% & 65.34\% & 86.04\% \\ Validation Accuracy & 53.66\% & 68.62\% & 61.80\% & 78.51\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Final network architecture used: Layers up to CONV2 are provided by CSP Darknet-53 [22]. Each convolutional layer is supported by the Mish activation function, which outperformed Leaky ReLU on the CSPDarknet-53 backbone [34]. Notice the feature pyramid net concatenating to form a 26 \(\times\) 26 \(\times\) 2048 volume on the second upscaling. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer** & **Output** & **Function** \\ \hline INPUT & 812 \(\times\) 812 \(\times\) 3 & \\ CONV1 & 812 \(\times\) 812 \(\times\) 64 & Darknet CONV1 \\ POOL1 & 406 \(\times\) 406 \(\times\) 64 & Darknet POOL1 \\ POOL2 & 406 \(\times\) 406 \(\times\) 32 & Darknet POOL2 \\ POOL3 & 406 \(\times\) 406 \(\times\) 64 & Darknet POOL3 \\... &... &... \\ CONV2 & 203 \(\times\) 203 \(\times\) 128 & Convolution 1 \(\times\) 1 of Darknet Block \\ UP1 & 26 \(\times\) 26 \(\times\) 1024 & Upsample 2 \(\times\) 2 \\ CONCAT1 & 102 \(\times\) 102 \(\times\) 256 & Concatenate CONV1 \\ UP1-CONVA & 51 \(\times\) 51 \(\times\) 512 & Convolution 3 \(\times\) 3 \\ UP1-CONVB & 51 \(\times\) 51 \(\times\) 512 & Convolution 3 \(\times\) 3 \\ UP1-CONVC & 51 \(\times\) 51 \(\times\) 512 & Convolution 3 \(\times\) 3 \\ UP2 & 26 \(\times\) 26 \(\times\) 2048 & Upsample 2 \(\times\) 2 \\ CONCAT2 & 203 \(\times\) 203 \(\times\) 64 & Concatenate CONV2 \\ UP2-CONVA & 52 \(\times\) 52 \(\times\) 256 & Convolution 3 \(\times\) 3 \\ UP2-CONVB & 52 \(\times\) 52 \(\times\) 256 & Convolution 3 \(\times\) 3 \\ UP2-CONVC & 52 \(\times\) 52 \(\times\) 256 & Convolution 3 \(\times\) 3 \\ CONV3 & 26 \(\times\) 26 \(\times\) 512 & Convolution 3 \(\times\) 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Fine-tuning: training accuracy. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Platform** & **Simulated** & **Testable Variety** & **Physical Location** & **3D Environmental** & **3D Model** & **Integration of** \\
**Platform** & **Geographic** & **of UAVs** & **Requirement** & **Models** & **Placement** & **Machine** \\  & **Location (Y/N)** & **(Wide/Limited)** & **(Y/N)** & **(Y/N)** & **(Manual/Automatic/NA)** & **Learning (Y/N)** \\ \hline OpenUAV [2] & N & Wide & N & Y & Manual & Y \\ Autonomous [3] & N & Limited & N & Y & Manual & N \\ Airship Sim & & & & & \\ AirSim [4] & N & Wide & N & Y & Automatic & Y \\ UAS Collision [5] & Y & Wide & N & N & NA & N \\ Avoidance Sim & & & & & \\ Lincoln Lab Sim [6] & Y & Wide & Y & Y & Manual & Y \\ Vision-Based [7] & N & Limited & N & Y & Manual & Y \\ UAV Platform & & & & & \\ Our Platform & Y & Wide & N & Y & Automatic & Y \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison results. Firstly, the raw satellite image is laid as a ground plane, respecting the relative physical scale of objects in the image. The machine learning algorithms then spawn the relevant objects within Gazebo with their appropriate dimensions and rotation, as illustrated in Figure 15a-c. The environment is ready for testing under the deployment of UAVs. At present, the simulation is minimalist; all buildings are abstracted to a single house, and all trees to a single oak tree.