Dual-Domain Prior-Driven Deep Network for Infrared Small-Target Detection

In recent years, data-driven deep networks have demonstrated remarkable detection performance for infrared small targets. However, continuously increasing the depth of neural to enhance has proven impractical. Consequently, integration prior physical knowledge related targets within become crucial. It aims improve models’ awareness inherent characteristics. this paper, we propose a novel dual-domain prior-driven network (DPDNet) small-target detection. Our method integrates advantages both and model-driven methods by leveraging characteristics as driving force. Initially, utilize sparse boost their saliency at input level network. Subsequently, high-frequency feature extraction module, seamlessly integrated into network’s backbone, is employed excavate information. DPDNet simultaneously emphasizes in spatial domain frequency domain. Compared with previous CNN-based methods, our achieves superior while utilizing fewer convolutional layers. 78.64% IoU, 95.56 Pd, 2.15 × 10−6 Fa on SIRST dataset.