A Wide and Shallow Network Tailored for Infrared Small Target Detection

Designing lightweight yet competitive models remains a challenging problem across the computer vision community—Infrared Small Target Detection (IRSTD) is no exception. To address this challenge, we propose WSNet, a novel model that achieves competitive performance while significantly reducing computational cost and memory consumption, without relying on deeper architectures or complex fusion mechanisms. The core innovation of WSNet lies in its extremely simple yet highly efficient network architecture, tailored to the specific demands of the IRSTD task. To the best of our knowledge, WSNet is the lightest existing model in the IRSTD field, containing only 0.054 M parameters—hundreds of times fewer than state–of–the–art alternatives—and requiring merely 1.050 G FLOPs. Extensive experiments on multiple benchmark datasets show that WSNet not only performs on par with leading methods but also delivers substantially faster inference speeds, making it highly suitable for real–time applications on embedded and resource–constrained devices.