# Infrared Ship Target Detection Based on Dual Channel Segmentation Combined with Multiple Features
## 1 Introduction
Numerous scholars have made significant contributions to the field of infrared ship target detection, resulting in notable achievements in various applications. Traditional algorithms for infrared ship target detection at sea can be categorized into single-frame and multi-frame detection based on time series. In the context of multi-frame detection methods, researchers such as [PERSON] et al. [16] and [PERSON] et al. [17] have successfully suppressed interference clutter by leveraging the temporal characteristics of the image sequence. The target remains relatively stable, while the interference clutter exhibits fluctuation patterns, enabling effective target detection. However, this approach limits adaptability in scenarios with fixed interferences, such as islands in the sea surface background. Additionally, the computation of multi-frame images is time-consuming, and it cannot detect targets that lack continuous multi-frame information. Regarding single-frame detection algorithms, there are relatively more approaches available. Different researchers have analyzed and described infrared ship targets from various perspectives, including the grayscale difference between targets and backgrounds, the combination of multiple target features, frequency domain characteristics of images, and saliency maps of images. Based on these analyses, researchers have designed different algorithms to achieve target detection. A detection method based on multi-resolution difference filtering combined with a selective search algorithm was proposed by [PERSON] et al. [18]. This method utilizes the results of multi-resolution difference filtering and a selective search algorithm to obtain the detection outcome. However, this algorithm is typically suitable for scenarios with simple backgrounds, uniformity, and significant grayscale differences between targets and backgrounds. In the presence of complex backgrounds such as bright and dark patches on the sea surface, the effectiveness of multi-resolution difference filtering may deteriorate significantly. [PERSON] et al. [19] proposed a detection method based on multi-scale local edge gradients, leveraging the characteristic distribution of sea clutter and the sea horizon, which typically exhibit significant vertical gradients and nearly zero horizontal gradients. In contrast, ships of specific sizes show substantial gradients in horizontal and vertical directions. To achieve accurate detection, this method relies on an effective preprocessing technique with exceptional clutter suppression capabilities. However, it is worth noting that currently, there is no algorithm available that can effectively suppress the abundance of bright and dark fluctuating sea clutter under sunlight conditions. [PERSON] et al. [20] employed the mean shift algorithm to filter an image and then calculated the mean gradient projection in the vertical direction to obtain the regions of interest for ship targets. Finally, they applied a row-column mean adaptive threshold segmentation algorithm to segment the infrared ship targets against a background with intense sunlight reflection. Although this method demonstrated good detection results in the tested scenarios, it relies on the accurate extraction of the locating rows. If the sea surface exhibits intense and widespread sunlight reflection, the estimation of locating rows based on gradients can be prone to errors. Based on the analysis of the geometric features, grayscale statistical features, and texture features of local regions in the sky, waves, and targets, [PERSON] et al. [21] proposed a ship target recognition technique, which utilized a combination of multiple features. Although the use of principal component analysis reduced the dimensionality of the feature vectors, the training and classification process still required considerable time. Additionally, in scenarios with fish-scale waves, this method exhibited a higher false alarm rate. [PERSON] et al.