# An Enhanced Aircraft Carrier Runway Detection Method Based on Image Dehazing
## 1 Introduction  
Due to the needs of modern national security and interests development, aircraft carriers, as mobile platforms for aircraft takeoff and landing at sea, play a crucial role. Meanwhile, with the advancement of technology, whether in civilian or military scenarios, an increasing number of hardware devices are trending towards unmanned development. A CUAV deployed on ships, e.g., aircraft carriers and destroyers, is gradually playing vital roles in various aspects such as maritime rescue, reconnaissance, surveillance, target engagement, etc. Therefore, as a crucial aspect of the entire mission execution of shipborne CUAVs, CUAV landing has gradually become a popular research field in recent years. Typically, CUAV landing methods mostly rely on sensors such as vision, altitude, and laser, with vision sensors gradually becoming the most commonly used sensor type in this neighborhood due to their low cost and high accuracy [1]. One of the most crucial subtasks in landing missions is runway line detection and positioning. For runway detection, there are in convention two kinds of methods, which are the traditional image processing methods [2; 3] and deep learning methods [4; 5]. Traditional image processing methods typically rely on image edge detection and often require the assumption of multiple ideal prior conditions, making them unsuitable for practical runway line detection. With the rapid advancements in neural networks over the past few years, runway detection algorithms for unmanned devices increasingly utilize convolutional neural network (CNN) algorithms. These algorithms [6; 7; 8; 9] can achieve a well-generalizedmodel by feeding a large amount of data into the network structure for forward propagation and then fine-tuning the model's parameters through backpropagation using a loss function. In practical applications, these models are typically pruned and quantized before deployment onto hardware devices. They utilize camera data for inference to obtain reference results, enabling full process automation without human intervention in controlling device actions. Although these methods have achieved satisfactory runway detection results, carrier operations at sea often encounter adverse weather conditions such as rain and haze. This leads to degradation in the image data acquired by visual sensors, which inevitably affects the precision of CUAV runway detection. Undoubtedly, this increases the complexity and risk during CUAV landing missions. In recent years, there has been a surge in work [10,11] on outdoor hazed image restoration. However, these methods often focus solely on the quality of image restoration without considering the feasibility of their practical application scenarios. This has led to an excessive number of model parameters and increased computational time for these methods. This paper focuses on the problem of degradation in runway line recognition due to image sensor degradation in haze conditions and proposes a runway line recognition system based on image dehazing. In summary, the contributions of our paper can be mainly summarized as follows:  
1. A lightweight dehazing network that combines CNNs with Transformers structure is introduced. An effective complementary attention module and a novel Transformer of linear computation complexity are designed to enhance the network's capability in image restoration;
2. By analyzing the positions of pixels obtained from the runway detection algorithm in two-dimensional space, adjusting the landing process of CUAVs by combining sensor data, performing multiple spatial transformations, and localization the spatial straight-line expressions of runway lines, a runway line localization method is proposed;
3. A system framework is designed to address the issue of CUAVs experiencing difficulty in safely and reliably landing in hazed conditions at sea. In the Airsim simulation environment, we conduct extensive experiments.