Local Patch Network With Global Attention for Infrared Small Target Detection

Infrared small target detection plays an important role in the infrared search and tracking applications. In recent years, deep learning techniques have been introduced to this task and achieved noteworthy effects. Following general object segmentation methods, existing deep learning methods usually process the image from the global view. However, the locality of small targets and extreme class-imbalance between the target and background pixels are 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 article 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 an LPNet. By leveraging both the global and local properties, the data-driven framework proposed in this article has the ability of fusing multiscale features for small target detection. Extensive experiments on synthetic and real datasets show that the proposed method achieves the state-of-the-art performance in comparison with both traditional and deep learning methods.