Feature Multi-Scale Enhancement and Adaptive Dynamic Fusion Network for Infrared Small Target Detection

Abstract

This study aims to address a series of challenges in infrared small target detection, particularly in complex backgrounds and high-noise environments. In response to these issues, we propose a deep learning model called the Feature Multi-Scale Enhancement and Adaptive Dynamic Fusion Network (FMADNet). This model is based on a U-Net architecture and incorporates a Residual Multi-Scale Feature Enhancement (RMFE) module and an Adaptive Feature Dynamic Fusion (AFDF) module. The RMFE module not only achieves efficient feature extraction but also adaptively adjusts feature responses across multiple scales, further enhancing the detection capabilities for small targets. Additionally, the AFDF module effectively integrates features from the encoder and decoder during the upsampling phase, enabling dynamic learning of upsampling and focusing on spatially important features, significantly improving detection accuracy. Evaluated on the NUDT-SIRST and IRSTD-1k datasets, our model exhibits strong performance, showcasing its effectiveness and precision in identifying infrared small targets in diverse complex environments, along with its remarkable robustness.