DMFNet: Dual-Encoder Multistage Feature Fusion Network for Infrared Small Target Detection

Infrared small target detection (IRSTD) is the challenging task of identifying small targets with low signal-to-noise ratios in complex backgrounds. Traditional methods in the complex background of IRSTD lead to a large number of false alarms and misdetections. Although CNN-based methods have made progress in IRSTD, how to extract more effective information and fully utilize interlayer information remains an unresolved issue. Therefore, this article proposed a dual-encoder multistage feature fusion network (DMFNet). Specifically, we designed a dual-encoder with different inputs to capture more effective small target feature information. We then designed a receptive field expansion attention module (REAM) to incorporate nonlocal contextual information. In the decoding phase, the Triple Cross-layer Fusion Module (TCFM) was developed to exchange the low-level spatial details and the high-level semantic information for preserving more small target information in deeper layers. Finally, by concatenating multiscale features from various layers of the decoder, more discriminative feature maps were generated to clearly describe the infrared small targets. Experimental results on the NUDT-SIRST, NUAA-SIRST, and IRSTD-1k datasets demonstrated that DMFNet outperforms some other state-of-the-art methods, achieving superior detection performance. The codes are available at https://github.com/BJZHOU2000/DMFNet.