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

• Published in: IEEE transactions on aerospace and electronic systems Vol. 59; no. 4; pp. 1 - 13
• Main Authors: Zhang, Tianfang, Li, Lei, Cao, Siying, Pu, Tian, Peng, Zhenming
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Context; Context module; Correlation; Feature extraction; Feature fusion; Fuses; Infrared small targets; Modules; Neural networks; Object detection; Pyramid context network; Semantics; Target detection; Task analysis
• ISSN: 0018-9251; 1557-9603
• DOI: 10.1109/TAES.2023.3238703

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 https://github.com/Tianfang-Zhang/AGPCNet .