Fast and robust small target detection is one of the key technologies in the infrared (IR)
search and tracking systems. With the development of deep learning, there are many data …

Infrared small target detection plays an important role in target warning, ground monitoring,
and flight guidance. Existing methods typically utilize local-contrast information of each pixel …

Infrared small target detection in complex scenes is an important but challenging research
hotspot in infrared early warning fields. Previous studies have proved that low-rank Tucker …

Aerial moving target detection is an important technology to ensure the safety of civil flight
and airspace. Different from ground-based active radar and space-based visible imager, on …

Existing infrared (IR) small target detection algorithms often lack adaptability in complex
scenes and heavily rely on parameter configurations. To address this limitation, we propose …

Existing low-rank methods usually introduce manifold learning to achieve good detection
performance in complex scenes. However, these methods suffer from high computational …

Most current infrared small target detection methods attempt to fuse local and global
information by using single-scale inputs and creating a multiscale feature pyramid during …

Low-rank and sparse decomposition (LRSD)-based models are powerful and robust tools
for infrared small target detection. However, due to the calculation of singular value …

A novel approach is proposed in this study that combines superpixel (SP) segmentation and
multiclassifier ensemble learning (EL) to address the limited availability of labeled samples …

Infrared small target (IRST) detection aims at separating targets from cluttered background.
Although many deep learning-based single-frame IRST (SIRST) detection methods have …