## 1 Introduction  
Infrared ship target detection is an important technology for maritime search and track applications [1; 2], where both accuracy and robustness are indispensable. However, because of the long imaging distance, for thermal infrared (TIR) images, the signal intensity of a small ship target is usually very weak without sufficient texture and structure information. More importantly, the complicated sea clutter such as sun glint, tail wave, island, sea fog and sea-sky line are usually capricious without predictable shape, which reduces the accuracy of TIR ship target detection. Moreover, the variable size and irregular shape of a ship target also further restrict the robustness of target detection. For above-mentioned reasons, infrared ship detection has attracted many researchers and a number of ship target detection algorithms are designed [3; 4; 5]. The representative TIR ship detection algorithms can be roughly divided into two categories, namely, detect-before-track (DBT) strategy and track-before-detect (TBD) strategy. By taking full advantage of the continuity of moving target and the stationarity of background, the TBD-based detection methods, such as frame difference method [6], three-plot correlation filter [7] and multi-stage hypothesis test filter [8], have achieved outstanding performance for the ships with continuous trajectory. However, in many maritime scenes, the background is changeable, and the target trajectory might be discontinuous, so the performance of TBD methods would degrade sharply. Compared with TBD strategy-based methods, the DBT-based methods have many advantages, such as less prior knowledge and faster computational speed and can work stably for the target without continuous trajectory under variable background. Therefore, the DBT-based ship detection methods are of great significance and have been drawing much attention from researchers recently. The existing DBT-based ship detection methods can be approximately classified into four categories: The target/background modeling-based methods, the image segmentation-based methods, the human visual system (HVS)-based methods, and the machine learning-based methods. In the target/background modeling-based methods, the ship target and background are separated by modeling the TIR imaging properties of ship target or background. [PERSON] et al. [9] introduced an end-to-end temperature difference model for the detection and classification of TIR ships at the environmental conditions with an analysis-based scanning detector. [PERSON] et al. [10] proposed a TIR ship detection method by modeling ship radiation anomalies with a nonlinear statistical Gaussian mixture model. These methods try to extract the ship target from background via modeling on the premise of acquiring abundant knowledge about the infrared radiation characteristics of ship target and background, but it is difficult to meet these requirements. To solve this problem, [PERSON] et al. [11; 12] estimated the background and distinguished the target from sea clutter by using the heterogeneous background removal filter. Furthermore, the statistical histogram curve transforms were also developed for the infrared maritime target detection based on the model assumption that the ship target region is much brighter than the background, such as the methods mentioned in [13; 14]. These background removal filters and statistical histogram curve transforms are excellent for the infrared point ship target with relatively high positive contrast. However, for low contrast or negative contrast, the ship targets are dim, and the background clutter is intricate, so these methods may rapidly reduce the detection performance. The image segmentation-based methods have the advantages of simplicity and efficiency, which are widely used for ship detection in TIR images. The classical threshold segmentation methods such as 2D Otsu [15], minimum error [16], and 2D maximum entropy [17] are well known in infrared ship target segmentation for their simplicity and easy-implementation.