MSSLNet: Multiscale Sparse Large-Kernel Net for Infrared Small Target Detection

Infrared small target detection (IRSTD) is inherently challenging due to extremely low signal-to-noise ratios (SNR) in complex backgrounds. Effective detection necessitates capturing local context and suppressing noise to enhance target discrimination. To this end, we propose MSSLNet, which significantly improves detection performance by minimizing irrelevant noise interactions and capturing multi-scale local context. Specifically, a Sparse Large-kernel Convolution Block (SLCB) is introduced, utilizing a sparse-dense dual-branch design to bolster target response while preserving critical information flow. Based on SLCB, a Multi-Scale Sparse Large-kernel Block (MSSLB) is developed to model contextual information for effective target-background separation, further enhanced by a Cross-Gated Module (CGM). Additionally, a Bi-directional Guided Fusion (BGF) module is proposed to optimize cross-layer detail-semantic fusion within skip connections. With only 0.293 M parameters and 2.99 GFLOPs, MSSLNet is highly efficient. Extensive experiments on the NUAA-SIRST and IRSTD-1K datasets demonstrate that our method achieves state-of-the-art performance in both accuracy and efficiency. The code is available at https://github.com/xiaodacheng01/msslnet-main.