MRF3Net: An Infrared Small Target Detection Network Using Multireceptive Field Perception and Effective Feature Fusion

Infrared small target detection (IRSTD) has made remarkable achievements in recent years. However, the core focus of current works lies on the philosophy of “increasing network complexity,” which leaves the crucial strategies behind performance improvement unclear. To handle this, we highlight two strategies of IRSTD: 1) multireceptive field perception and 2) effective feature fusion. Focusing on these strategies, we propose a multireceptive field perception and effective feature fusion network, named MRF3Net. Specifically, for multireceptive field perception, we devise a multiple perception encoder (MPE). For effective feature fusion, we devise a feature fusion encoder (FFE) and a feature fusion decoder (FFD). The former improves the encoding efficiency by reducing the interference information and preserving target details, and the latter fuses the target information of low-level and high-level features while eliminating noise. Experiments demonstrate that MRF3Net achieves state-of-the-art performance on popular public datasets while maintaining a fast inference speed of approximately 0.011 s/frame on a single NVIDIA GeForce 3070Ti GPU and 0.049 s/frame on the NVIDIA Jetson Orin. Notably, it significantly reduces parameter costs compared with the previous state-of-the-art approaches, validating its efficiency. Moreover, our MPE, FFE, and FFD have proved to be effective in enhancing other IRSTD approaches. Our code will be made available at: https://github.com/Temperature-ai/MRF3Net.