  Infrared small target detection plays an important role in the infrared
search and tracking applications. In recent years, deep learning techniques
were introduced to this task and achieved noteworthy effects. Following general
object segmentation methods, existing deep learning methods usually processed
the image from the global view. However, the imaging locality of small targets
and extreme class-imbalance between the target and background pixels were not
well-considered by these deep learning methods, which causes the low-efficiency
on training and high-dependence on numerous data. A local patch network (LPNet)
with global attention is proposed in this paper to detect small targets by
jointly considering the global and local properties of infrared small target
images. From the global view, a supervised attention module trained by the
small target spread map is proposed to suppress most background pixels
irrelevant with small target features. From the local view, local patches are
split from global features and share the same convolution weights with each
other in a patch net. By leveraging both the global and local properties, the
data-driven framework proposed in this paper has fused multi-scale features for
small target detection. Extensive synthetic and real data experiments show that
the proposed method achieves the state-of-the-art performance compared with
existing both conventional and deep learning methods.
