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 and perceive pixel correlations within and between patches at specific scales via local semantic association and global context attention, respectively. Then, the contextual information from multiple scales is fused by context pyramid module to achieve better feature representation. In the upsampling stage, we fuse the low and deep semantics through asymmetric fusion module 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.
Attention-Guided Pyramid Context Networks for Detecting Infrared Small Target Under Complex Background
IEEE Transactions on Aerospace and Electronic Systems ; 59 , 4 ; 4250-4261
2023-08-01
5391772 byte
Article (Journal)
Electronic Resource
English
INFRARED SMALL TARGET DETECTION ALGORITHM BASED ON SELF-ADAPTIVE BACKGROUND FORECAST
British Library Online Contents | 2006
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