# An Enhanced Aircraft Carrier Runway Detection Method Based on Image Dehazing
## 2 Related Work
### Runway Detection  
The runway detection method is a crucial part of the autonomous driving system. It is mainly divided into three categories: classification-based, object detection-based and segmentation-based methods [27]. Classification-based methods like DeepLanes [6], assume some location-dependent prior knowledge is datum and based on it to design a CNN architecture. Object detection-based methods like EELane [7] propose an empirical evaluation framework that integrates large-scale data collection with deep learning algorithms to achieve real-time vehicle and lane detection. VPGNet [8] proposes a VPGNet architecture that integrates a vanishing point prediction task to enhance the robustness of lane and road marking detection under various weather and illumination conditions, setting a new benchmark for multi-task learning in autonomous driving scenarios. STLNet [9] introduces a spatiotemporal deep learning approach for real-time lane detection that leverages both spatial and temporal constraints to enhance accuracy and robustness under various weather and traffic conditions. The aforementioned algorithms successively addressed the limitations of previous ones. Despite achieving satisfactory runway detection performance, the effectiveness and robustness are somewhat compromised by the constraints imposed by the inverse perspective transformation (IPM) process during post-processing. Segmentation-based methods like [28] perform a CNN-based regression approach for multi-lane detection and classification that focuses on producing parameterized lane information from image coordinates. Ref. [29] proposes a color-based segmentation approach for robust and efficient lane detection under various environmental conditions without relying on complex vision algorithms or extensive parameter tuning. Ref. [4] introduces a Spatial CNN (SCNN) architecture that enhances convolutional neural networks' ability to capture spatial relationships between pixels for improved traffic scene understanding. Ref. [30] introduces a Recurrent Feature-Shift Aggregator (RESA) that leverages strong shape priors of lanes and captures spatial relationships of pixels across rows and columns through recurrent shifting of sliced feature maps, along with a Bilateral Up-Sampling Decoder for refining pixel-wise predictions in lane detection tasks. 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. In recent years, there have also been some row-based algorithms that assume a line of runway anchors in the fixed row direction. Such as, CLRNet [31] employs learnable anchor parameters, started from \(x_{0},y_{0},\theta_{0}\), and runway length \(l_{0}\). LaneFormer [32] introduces a novel Transformer with \(x\) and \(y\) axis attention mechanism to detect runway instances. UFLD [5] leverages the flattening operation to convert the 2D feature map into a 1D vector, and then the row-wise runway point positions are detected by the 1D feature map. In our work, we adopt the UFLD algorithm to detect runway lines in the images. The algorithm is lightweight and fast, which is suitable for real-time applications. In addition, for applications in hazed maritime scenarios, we have adjusted the confidence modification based on the quality of the dehazed images, altering the hyperparameters set during training. This enhancement aims to improve the detection robustness in haze conditions.