# Infrared Ship Target Detection Based on Dual Channel Segmentation Combined with Multiple Features
## 1 Introduction  
Ships play a crucial role as the primary carriers of human maritime activities, and the safeguarding of sovereignty and maritime rights in territorial waters relies on the effective monitoring of ship targets on the sea surface. The accurate detection of ship targets at sea is essential in various contexts, including military operations for identifying potential threats, civilian applications such as ship monitoring, and search and rescue operations for shipwrecks. The methods for detecting ship targets in the marine environment can be broadly categorized into shore-based and space-based approaches. Shore-based detection methods encompass infrared imaging and visible light imaging. On the other hand, space-based detection methods include remote sensing techniques such as visible light imaging, infrared imaging, and synthetic aperture radar (SAR) imaging [1]. Visible light imaging offers high resolution and detailed information [2] but it is limited in its ability to operate effectively under all weather conditions. Despite extensive research on the remote sensing-based detection of ship targets on the sea surface [3, 4, 5, 6], several challenges still need to be addressed. Remote sensing images are susceptible to cloud interference, and broken cloud patterns often resemble actual targets resulting in a high rate of false alarms. Additionally, remote sensing images are more suitable for open sea scenes due to their long imaging distances, which often leads to missed detections of small ship targets. False detections can also occur due to the presence of islands, reefs, and shore-based structures. In the case of remote sensinginfrared images, the temperature difference between day and night often leads to grayscale inversions between the background and the target. These challenges significantly limit the applicability of existing algorithms in practice. Deep learning-based object detection algorithms in the mainstream are primarily classified into two-stage algorithms and one-stage algorithms. Representative algorithms for the two-stage approach include R-CNN [7] and faster R-CNN [8]. These algorithms involve candidate region extraction, candidate region classification, and regression as the main processes. On the other hand, one-stage algorithms, exemplified by the SSD [9] and YOLO series [10], transform object detection into a regression problem for determining target position and category information. This approach is faster than the two-stage algorithms. Based on deep learning techniques, numerous researchers have made improvements to the respective algorithms in their specific research fields to detect ship targets at sea [11; 12; 13; 14]. These advancements have yielded better detection results compared to comparative algorithms. However, deep learning necessitates a significant amount of training samples, while the available offshore ship datasets are limited in size. Furthermore, the complex sea surface background poses challenges in obtaining models with wide applicability. To address the limitation of deep learning algorithms requiring a large number of samples for model training, deep transfer learning techniques can be employed to mitigate the scarcity of infrared target datasets for ships at sea. For instance, [PERSON] et al. [15] utilized a significant amount of simulated infrared ship images for initial model training, followed by fine-tuning the trained model using a smaller quantity of actual infrared ship data. While this approach helps alleviate the demand for real infrared ship datasets, it still necessitates substantial computational resources. Additionally, the radiometric characteristics of simulated images may not entirely align with those of actual ship images, thereby increasing the complexity of the transfer learning process. Infrared imaging technology is extensively utilized for target detection due to its compact size, simple structure, high concealment, and ability to operate in all weather conditions.