# Multiscale Feature-Based Infrared Ship Detection
###### Abstract  
In this paper, based on the idea of \"step-by-step accuracy\", a novel multiscale feature-based infrared ship-detection method (MSFISD) is proposed. The proposed method can achieve efficient and effective infrared ship detection in complex scenarios, which may provide assistance in applications such as night surveillance. First, candidate regions (CRs) are extracted from the whole image by extracting the sea-sky line and region of interest (ROI). The real sea-sky line is extracted based on the gradient features enhanced by large-scale gradient operators. The coarse segmentation results are obtained by the optimization method and are then refined by incorporating the edge features of the ship to reduce false alarms and obtain the CRs. Second, by analyzing the shape features of ships, the feature quantity is established, and the ships in CRs are finally accurately segmented. Experimental results demonstrate that compared with the other five methods, the proposed method has higher detection accuracy with a lower false-alarm rate and performs better in complex sea scenarios.  
sea-sky line extraction; multiscale feature; maximum correlation criterion; feature-based threshold; infrared ship detection +
Footnote †: journal:  
This method introduces the contour features of the ship in order to assist the segmentation while dealing with the complex sea surface background; disturbances such as islands, reefs, and clutter have similar contour characteristics to the target, which may lead to false alarms as well as the selection of the initial contour; the number of iterations will also affect the detection accuracy.  
The key to the feature-analysis method is to select suitable features, not only to ensure that the features of the target itself are prominent and the difference between the target and the background is noticeable, but also to assess the difficulty and calculate the amount of feature extraction. In recent years, the deep-learning method has been widely studied with many detection algorithms proposed. Such methods face the problem of insufficient samples, and the number of network layers and parameter settings will directly affect the final detection accuracy and efficiency. In addition, a common issue in the existing methods is that it is hard to ensure the efficiency and real-time performance of target detection in global images; this remains an ongoing research challenge.  
In view of the above problems, this paper proposes an infrared ship-detection method based on the idea of \"step-by-step accuracy\", while considering various characteristics of the sea background and ships. First, CRs are extracted from the whole image by extracting the sea-sky line and ROI, and then, the final ship target extraction is accomplished by incorporating the shape features of the ship. The main purpose of this study is to improve the performance of infrared ship detection in complex scenarios, ensuring both effectiveness and high processing efficiency. The rest of this paper is organized as follows. In Section 2, the details of the proposed sea-sky line extraction and feature-based target extraction method are described. In Section 3, the experimental results, including the comparisons between the proposed method and the other five methods, are presented. Finally, the conclusion is presented in Section 4.