Learning-based infrared small object detection methods currently rely heavily on the
classification backbone network. This tends to result in tiny object loss and feature …

Compared with radar and visible light imaging, infrared imaging has its own unique
advantages, and in recent years, it has become a topic of intense research interest. Robust …

The detection of small infrared targets with low signal-to-noise ratios (SNRs) and contrasts in
noisy and cluttered backgrounds is challenging and therefore a domain of active research …

Excellent performance, real time and strong robustness are three vital requirements for
infrared small target detection. Unfortunately, many current state-of-the-art methods merely …

Many state-of-the-art methods have been proposed for infrared small target detection. They
work well on the images with homogeneous backgrounds and high-contrast targets …

Infrared small target detection plays an important role in military and civilian fields while it is
difficult to be solved by deep learning (DL) technologies due to scarcity of data and strong …

Efficient detection of targets immersed in a complex background with a low signal-to-clutter
ratio (SCR) is very important in infrared search and tracking (IRST) applications. In this …

Recently, infrared small target detection problem has attracted substantial attention. Many
works based on local low-rank model have been proven to be very successful for enhancing …

The detection of small infrared targets lacking texture and shape information in the presence
of complex background clutter is a challenge that has attracted considerable research …

Robust and effective detection of small target and false alarm (FA) suppression are the key
techniques in infrared search and track systems. In this paper, the derivative entropy-based …