Attention-Guided Pyramid Context Networks for Detecting Infrared Small Target Under Complex Background

Abstract

Infrared small target detection techniques remain a challenging task due to the complex background. To overcome this problem, by exploring context information, this research presents a data-driven approach called Attention-Guided Pyramid Context Network (AGPCNet). Specifically, we design Attention-Guided Context Block (AGCB) and perceive pixel correlations within and between patches at specific scales via Local Semantic Association (LSA) and Global Context Attention (GCA) respectively. Then the contextual information from multiple scales is fused by Context Pyramid Module (CPM) to achieve better feature representation. In the upsampling stage, we fuse the low and deep semantics through Asymmetric Fusion Module (AFM) to retain more information about small targets. The experimental results illustrate that AGPCNet has achieved state-of-the-art performance on three available infrared small target datasets. The source codes are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Tianfang-Zhang/AGPCNet</uri> .