Infrared small target detection is an important branch of computer vision applications. It has extensive applications in the military and civil fields

such as in precision guidance

security monitoring

and medical imaging systems. Infrared image preprocessing includes noise reduction and background suppression

and the main factor that affects small target detection is background clutter interference. Therefore

infrared image preprocessing prioritizes background suppression algorithms. Among numerous background suppression algorithms

the Robinson guard filter is a widely used filter based on a guard band. This filter cannot only achieve excellent suppression effect

but can also integrally retain the edge and internal information of small targets. However

the filtering mechanism of the Robinson guard filter is more sensitive to noise and cannot effectively deal with small targets with varying sizes. Given the limitation of the Robinson guard filter

this study proposes a small target detection method based on infrared background suppression. First

the background of infrared images is suppressed via Tophat transformation of the mathematical morphology to decrease the influence of noise on followup processing and reduce the sensitivity of the filtering mechanism of the Robinson guard filter to noise. Second

the modified Robinson guard filter is adopted to further suppress the background and to enhance the small targets of the processed images. Then

the regions of interest are identified using an adaptive threshold method. On this basis of this method

Unger smoothing is performed to remove small noise points. Then

we apply the local signal-clutter ratio (SCR) to eliminate residual background clutter after considering the difference between the gray value of the small targets and the gray value of the background

Finally

a moving pipeline filter is applied based on the motion continuity of the infrared small targets to exclude false positives

and the locations of the true small targets are accurately confirmed. We use three groups of image sequences with different backgrounds as experimental objects. Furthermore

we provide filtering performance (e.g.

GSNRG and BSF) and detection performance (e.g.

detection rate and false alarm rate) as evaluation indices. Test results show that the proposed method outperforms the Tophat algorithm

the max-mean algorithm

the Robinson guard filter

and the improved local contrast measure (ILCM) algorithm in terms of background suppression and detection performance. Compared with the traditional Robinson guard filter

the proposed method cannot only retain the characteristics of the small targets significantly

but can also improve the detection rates of the three groups of image sequences by 1.1%

2%

and 11%

respectively. Moreover

it can decrease their false alarm rates by 14%

12%

and 16%

respectively. The proposed method can achieve superior detection performance and can fully satisfy real-time requirements based on the test results. The experiment results demonstrate that the proposed method exhibits better performance in terms of background suppression and small target detection than the other four algorithms. The method also exhibits good adaptability in low SCR images. Meanwhile

it possesses high capability for real-time processing

which is conducive to realizing technological applications.