DMFNet: Dual-encoder Multi-stage Feature Fusion Network for Infrared Small Target Detection

• Published in: IEEE transactions on geoscience and remote sensing p. 1
• Main Authors: Guo, Tan, Zhou, Baojiang, Luo, Fulin, Zhang, Lei, Gao, Xinbo
• Format: Journal Article
• Language: English
• Published: IEEE 11.03.2024
• Subjects: decoder-encoder; Decoding; Deep learning; Feature extraction; feature fusion; Infrared small target detection (IRSTD); multi-scale feature; Object detection; Robustness; Semantics; Task analysis
• ISSN: 0196-2892
• DOI: 10.1109/TGRS.2024.3376382

Infrared Small Target Detection (IRSTD) is a 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 missdetections. Although CNN-based methods have made progress in IRSTD, how to extract more effective information and fully utilize inter-layer information remains an unresolved issue. Therefore, this paper proposed a Dual-Encoder Multi-stage 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 non-local 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 multi-scale 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. Codes: https://github.com/BJZHOU2000/DMFNet.