Dim and small target detection algorithm based on multi-velocity hypothesis and energy accumulation

Detecting fast-moving, faint, and small targets with low signal-to-noise ratio (SNR) poses a significant challenge. This paper firstly adopts the multi-velocity hypothesis to make the energy of dim and small targets accumulate effectively and suppress the noise. Subsequently, post-energy accumulation, line detection is conducted on the resultant trailing line to ascertain the actual speed of target movement. Through target accumulation recovery, the centroid-based line correlation contrast method (CLPCM) algorithm is employed to suppress background clutter, thus enhancing the target's contrast. Finally, the target can be extracted by threshold segmentation. The experimental results demonstrate that, when compared with four classical and advanced small target detection algorithms, the proposed algorithm adeptly addresses the challenges associated with detecting diminutive, swiftly moving targets. Notably, it exhibits superior detection efficacy, particularly in scenarios characterized by low signal-to-noise ratios.