# Infrared Ship Target Detection Based on Dual Channel Segmentation Combined with Multiple Features
## 1 Introduction
[22] proposed a segmentation method based on energy maps by leveraging the thermal infrared characteristics of infrared images at sea. This method utilizes the energy difference between the background and targets. It can effectively segment targets when there is minimal background interference. However, in scenarios with large waves and significant grayscale variations in clutter, the practical segmentation performance of this method may not be ideal. Fractional Fourier transform, also known as the fractional order Fourier transform, is a mathematical transform that generalizes the classical Fourier transform. It provides a rich representation of spatial frequency information. [PERSON] et al. [23] utilized the differences in fractional Fourier frequencies between the target and the background to localize the vertical position of the target. This method showed good performance in locating bright targets but yielded poor results for dark targets. Furthermore, they discovered that modeling the fluctuating sea background in the frequency domain using Gaussian distributions was more stable, compared to modeling it in the spatial domain using pixel values. Therefore, they proposed a method [24] that utilizes Gaussian distribution to model the background in the Fourier domain. However, this method has numerous parameters, most of which are non-adaptive, making it challenging to apply in practical scenarios. In terms of computing target saliency maps, both the image feature algorithm proposed by [PERSON] et al. [25] and the visual attention model (VAM) proposed by [PERSON] et al. [26] have achieved good results. Based on these methods, many researchers have proposed using them to assist in target detection under complex background conditions in maritime environments. [PERSON] et al. [27] used the algorithm proposed by [PERSON] et al. to compute the saliency regions of the image after background filtering. By combining multiple shape features and iteration threshold segments, they achieved target segmentation. [PERSON] et al. [17] employed the visual attention model to obtain salient regions in consecutive frames. They combined the spatial saliency of the target with temporal consistency to distinguish real targets from salient interference regions, enabling ship detection under strong sunlight reflection on the sea surface. [PERSON] et al. [28] improved the visual attention model for target detection in infrared backlight environments and successfully detected ship targets under backlight conditions. Although the aforementioned methods can achieve good results in ship detection under strong daylight conditions, they are prone to false alarms when there are large areas of waves on the sea surface or bright/dark bands traversing the image. [PERSON] et al. [29] proposed a detection method based on morphological reconstruction and multi-feature analysis. They first performed grayscale morphological reconstruction on the original image and calculated and fused intensity feature maps. This approach suppressed sea clutter and enhanced the targets, enabling candidate target segmentation. Multiple features were then used to restrict and identify the candidate targets. Although this algorithm can recognize bipolar targets, it cannot effectively identify targets when there is interference between bright or dark patches of similar size to the target region, leading to certain false alarms. Similarly, utilizing saliency detection, [PERSON] [30] proposed a dual-channel image separation algorithm based on combining local grayscale dynamic range saliency mapping for nighttime maritime ship target detection. This algorithm can effectively detect small targets in nighttime ocean images. In summary, although there is a considerable amount of research on infrared ship target detection in maritime environments, most of it focuses on relatively simple scenes.