# An Enhanced Aircraft Carrier Runway Detection Method Based on Image Dehazing
## 3 Materials and Methods  
To enhance the airborne landing safety of carrier-based unmanned aerial vehicles in maritime hazed conditions, we have devised the method pipeline as illustrated in Figure 1. This pipeline primarily comprises three segments: image dehazing, runway line detection, and runway line localization, followed by the assessment of runway line localization error. Therefore, for a coherent exposition of our approach, this section predominantly expounds in three parts. Firstly, we provide a detailed analysis and explanation of the dehazing model architecture designed in this paper, along with each module. Secondly, we derive and elucidate the formulas for the runway localization method and error analysis adopted in this paper. In the end, we supplement the description of the environmental construction steps and data collection methods for the dataset used in this paper.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Method** & **Method** & **Category** & **Characteristics** \\ \hline \multirow{4}{*}{[6]} & \multirow{4}{*}{DeepLanes} & \multirow{4}{*}{classification-based} & \multirow{4}{*}{It get an advantage result of a complicated network structure. However, the prior position setting curbs its application in practical scenarios, and due to the complex network, the method is time-consuming.} \\ \cline{1-1} \cline{3-4}  & & & & By labeling regression bounding boxes or feature points for each lane segment, lanes can be efficiently detected by coordinate regression. However, due to the inverse perspective transformation (IPM) process during post-processing, its robustness is conditioned. \\ \hline \multirow{2}{*}{[7]} & \multirow{2}{*}{EELane} & \multirow{4}{*}{Object detection-based} & \multirow{2}{*}{By labeling regression bounding boxes or feature points for each lane segment, lanes can be efficiently detected by coordinate regression. However, due to the inverse perspective transformation (IPM) process during post-processing, its robustness is conditioned.} \\ \cline{1-1} \cline{3-4}  & & & & \\ \hline \multirow{2}{*}{[8]} & \multirow{2}{*}{CNN-based regression approach} & \multirow{2}{*}{segmentation-based} & \multirow{2}{*}{These methods typically operate on regions of individual pixels in the entire image, leveraging the paradigm of semantic segmentation to delineate runway lines within the image. But segmentation paradigm is inherently too strict, and the emphasis is on obtaining accurate classification per pixel rather than specifying the shape.} \\ \cline{1-1} \cline{3-4}  & & & & \\ \cline{1-1} \cline{3-4}  & & & & \\ \cline{1-1} \cline{3-4}  & & & & \\ \cline{1-1} \cline{3-4}  & & & & \\ \hline \multirow{2}{*}{[31]} & \multirow{2}{*}{CLRNet} & \multirow{2}{*}{row-based} & \multirow{2}{*}{These methods use anchor point detection set along a specific direction of the image (e.g., row direction), thus achieving good runway line detection results and real-time detection speed. However, these methods are highly dependent on image quality, and their detection performance significantly degrades when the images are hazy.} \\ \cline{1-1} \cline{3-4}  & & & & \\ \hline \multirow{2}{*}{[32]} & \multirow{2}{*}{LaneFormer} & \multirow{2}{*}{row-based} & \multirow{2}{*}{These methods use anchor point detection set along a specific direction of the image (e.g., row direction), thus achieving good runway line detection results and real-time detection speed. However, these methods are highly dependent on image quality, and their detection performance significantly degrades when the images are hazy.} \\ \cline{1-1} \cline{3-4}  & & & & \\ \hline \end{tabular}
\end{table}
Table 2: Summary of lane detection methods.  
Figure 1: The overall pipeline framework.