Training a convolutional neural network (CNN) to detect infrared small targets in a fully
supervised manner has gained remarkable research interests in recent years, but is highly …

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 …

Infrared target detection has important applications in rescue and Earth observation.
However, the disadvantages of low signal-to-clutter ratios and severe background noise …

Infrared dim-small target detection, as an important branch of object detection, has been
attracting research attention in recent decades. Its challenges mainly lie in the small target …

Despite that many state-of-the-art methods have been proposed for infrared (IR) small target
detection, target detectability (TD) and background suppressibility (BS) cannot be …

A global attention network (GANet) with multiscale feature fusion is proposed to detect
infrared small target by introducing a transformer attention module and an adaptive …

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

Network quantization is leveraged to reduce the model size, memory footprint, and
computational cost of deep neural networks. It is achieved by representing float weights and …

Infrared (IR) small target detection under complex environments is an essential part of IR
search and track systems. However, previously proposed IR small target detection …

Many existing low-rank methods have achieved good detection performance in uniform
scenes, but they suffer from a high false alarm rate in complex noisy scenes. Therefore, it is …