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