Abstract
To suppress background clutter and improve detection accuracy, we propose a dim target detection algorithm based on density peak search and region consistency. A density peak search algorithm is first applied to extract candidate targets, and these are then classified and marked according to the local mosaic probability factor, which is important in order to suppress the backgroundsssss clutter and accurately strip the candidate target region from the background. Based on the regional stability of the dim targets, local mosaic gradient factors are used to screen real targets from candidates, and a facet kernel filter is used to extract the irregular contours of dim targets with the aim of enhancing them. Our experimental results show that compared with existing algorithms, the proposed method has better detection accuracy and robustness in various complex scenarios.
Highlights
An infrared search and tracking system (IRST) has the advantages of good concealment and robust anti-interference ability, which is widely used in many aspects such as military early warning, precision guidance and remote sensing (Zeng et al 2006; Wang et al 2019)
In order to robustly detect the dim targets in complex backgrounds, this paper proposed an infrared dim target detection algorithm based on density peak search and region consistency
Candidate points are selected according to the proportional relationship between noise points and dim targets
Summary
An infrared search and tracking system (IRST) has the advantages of good concealment and robust anti-interference ability, which is widely used in many aspects such as military early warning, precision guidance and remote sensing (Zeng et al 2006; Wang et al 2019). The infrared target detection method is one of key technologies (Chen et al 2014). The dim target detection faces many challenges, such as the target only contains dozens of pixels in each frame, the dim targets miss texture and shape information, and the signal to clutter ratio of each frame is very low (Ye et al 2017; Huang et al 2019). The dim targets are often submerged in complex backgrounds (such as cloud edges, ocean waves, and high-brightness noise). The infrared dim target detection methods can be divided into single-frame-based detection methods (Dai et al 2017) and multi-frame-based detection methods (Lv et al 2018; Wan et al 2016; Zhang et al 2019). The single-frame-based methods have many advantages such as low complexity, high execution efficiency and strong real-time performance. The single-frame-based methods have attracted more attention
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