APAFNet: Single-Frame Infrared Small Target Detection by Asymmetric Patch Attention Fusion
Ziming Wang, Jianwei Yang, Zongxu Pan, Yuhan Liu, Bin Lei, Yuxin Hu
Abstract
Single-frame infrared small target (SIRST) detection has been applied in many civilian and military applications and has greatly developed since deep-learning methods grow in recent years. However, the contradiction between semantic information and spatial details limits detection performance, especially for dim and small targets with complex interference. To overcome the restrictions, we propose an asymmetric patch attention fusion network (APAFNet) in this letter to merge high-level semantics and low-level spatial details, which consists of an APAF module based on a patch channel attention branch and a dilation context block, guiding the network to collect local semantics and spatial details. The experimental results on the NUAA-SIRST dataset and IRSTD-1k dataset show that the proposed APAFNet can achieve excellent performance under complex backgrounds.