In recent years, the detection of infrared small targets using deep learning methods has garnered substantial attention due to notable advancements. To improve the detection capability of small targets, these methods commonly maintain a pathway that preserves high-resolution features of sparse and tiny targets. However, it can result in redundant and expensive computations. To tackle this challenge, we propose SpirDet, a novel approach for efficient detection of infrared small targets. Specifically, to cope with the computational redundancy issue, we employ a new dual-branch sparse decoder to restore the feature map. Firstly, the fast branch directly predicts a sparse map indicating potential small target locations (occupying only 0.5\% area of the map). Secondly, the slow branch conducts fine-grained adjustments at the positions indicated by the sparse map. Additionally, we design an lightweight DO-RepEncoder based on reparameterization with the Downsampling Orthogonality, which can effectively reduce memory consumption and inference latency. Extensive experiments show that the proposed SpirDet significantly outperforms state-of-the-art models while achieving faster inference speed and fewer parameters. For example, on the IRSTD-1K dataset, SpirDet improves $MIoU$ by 4.7 and has a $7\times$ $FPS$ acceleration compared to the previous state-of-the-art model. The code will be open to the public.

翻译：近年来，基于深度学习的红外小目标检测方法因显著进展而受到广泛关注。为提升小目标检测能力，这些方法通常保留一条维持稀疏微小目标高分辨率特征的路径，但这可能导致冗余且昂贵的计算。针对这一挑战，我们提出SpirDet——一种用于红外小目标高效检测的新方法。具体而言，为解决计算冗余问题，我们采用一种新型双分支稀疏解码器来重建特征图。首先，快速分支直接预测指示潜在小目标位置的稀疏图（仅占图像面积的0.5%）；其次，慢速分支在稀疏图指示的位置进行细粒度调整。此外，我们基于下采样正交性设计了轻量化的DO-RepEncoder（结合重参数化机制），可有效降低内存消耗与推理延迟。大量实验表明，所提出的SpirDet在实现更快推理速度和更少参数的同时，显著优于现有最优模型。例如，在IRSTD-1K数据集上，SpirDet将$MIoU$提升4.7，并相较此前最优模型实现$7\times$的$FPS$加速。相关代码将开源。