# An Enhanced Aircraft Carrier Runway Detection Method Based on Image Dehazing
## 3 Materials and Methods
### Dataset  
The dataset was generated by Airsim (ver.1.8.1) [33], an open-source cross-platform simulator developed based on the Unreal Engine (ver.4.27). The excellent visual rendering power of Unreal Engine enables the simulator to produce high-quality visual simulation effects, making it suitable for a wide range of applications or research areas related to machine learning and artificial intelligence, such as deep learning, computer vision and automated visual data collection. The overall simulation utilizes the aircraft carrier Ronald Reagan (CVN-76) and the shipboard fighter J-15 to construct a landing scenario within an ocean environment. The carrier-based fighter took off from the deck of the aircraft carrier, flew to a distance of about 10 km from the carrier to enter the holding track, and then began to return, entering the landing track at a distance of about 5 km from the carrier, lowering its altitude to 300 m, lowering its altitude to 60 m at a distance of 800 m from the carrier, and finally gliding down to land on the carrier's runway. Throughout the landing process, the center of the camera was always pointed at the runway on the deck of the carrier from the moment the aircraft entered the landing pattern. The ground truth of the trajectory was recorded by Airsim.  
First, the geometric model of camera imaging needs to be established, that is, to obtain the camera's internal parameter matrix \(K\). The internal parameter matrix \(K\) can be expressed approximately as:  
\[K=\begin{bmatrix}f/s&0&w/2\\ 0&f/s&h/2\\ 0&0&0\end{bmatrix} \tag{7}\]  
where \(f\) represents the focal length, \(s\) represents the pixel size, and \(w\) and \(h\) represent the resolution of the image.  
The camera in AirSim uses a perspective projection, then the focal length \(f\) and the pixel size \(s\) can be expressed as:  
The parameters of the camera geometric model obtained through setup and computation, such as the camera internal reference matrix \(K\), distortion sparse and field of view are summarised in Table 3.  
Next, the attitude change from the camera coordinate frame to the world coordinate frame still needs to be solved, that is, solving for the camera's external parameter. We set a point directly in front of the deck of [PERSON] (CVN-76) as the origin of the world coordinate frame, and using the North-East-Down (NED) coordinate frame and right-handedness, the carrier's coordinates in the world coordinate frame are (\(-148.45\), \(47.2\), \(31.26\)). The body coordinate frame uses the Forward Right Down (FRD) coordinate frame and right-handedness, which means that the airplane body is forward as the x-axis, perpendicular to the x-axis to the right as the y-axis direction, and perpendicular to the x-axis and y-axis downward as the z-axis, as shown in the Figure 9, which are the side view, front view, and top view in sequence. As shown in the Figure 10, the area surrounded by the red line is defined as the runway area.  
The final dataset acquired in the Airsim simulation consisted of a visible image with a depth map at the exact same camera position, using Airsim's recording feature to continuously acquire the image and ground truth values.  
Figure 10: Runway area of [PERSON].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & \multicolumn{2}{c}{**Value**} \\ \hline Intrinsic parameters &
\begin{tabular}{c} \(\begin{bmatrix}732.35461496\\ 0\end{bmatrix}\) & \(\begin{bmatrix}0&640\\ 732.35461496&360\\ 0&0&1\end{bmatrix}\) \\ Radial distortion & \(\begin{bmatrix}0&0&0\end{bmatrix}\) \\ Tangential distortion & \(\begin{bmatrix}0&0\end{bmatrix}\) \\ FOV & \(\begin{bmatrix}70^{\circ}\\ 1280\times 720\end{bmatrix}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of camera model.  
Figure 9: Definition of body coordinate and three views of the J-15. **(a)** The side view of J-15. **(b)** The front view of J-15. **(c)** The top view of J-15.