TCNNISTD: a parallel transformer–CNN network for infrared small target detection

Abstract

Infrared small target detection is critical in applications such as missile guidance, security surveillance, and precision tracking. Conventional methods relying on manual feature engineering often fail under severe background clutter and noise, while deep learning-based methods tend to degrade small-target features in deep networks, especially in noisy conditions. We propose a novel dual-branch architecture that combines Transformers and CNNs in parallel for robust infrared small target detection, focusing on low-signature scenarios. The CNN branch uses large-kernel convolutions to expand the receptive field and enhance local feature discrimination, while the Transformer branch captures long-range global dependencies. An Adaptive Fusion (AF) module with dual attention (channel and spatial) dynamically optimizes feature representation by amplifying target-specific features and suppressing background noise. Experiments show that our method achieves competitive performance against state-of-the-art models, with higher Dice coefficient and mean Intersection-over-Union (mIoU) scores on the augmented NUAA-SIRST dataset, indicating more precise segmentation boundaries and improved robustness in cluttered environments. These advantages highlight the potential of our approach for general infrared small target detection tasks. The dual-branch design and adaptive fusion strategy make it well suited to mission-critical applications such as security surveillance, and precision tracking, and it may also extend to remote sensing and maritime surveillance.