Infrared small target (IRST) detection is challenging in simultaneously achieving precise, robust, and efficient performance due to extremely dim targets and strong interference. Current learning-based methods attempt to leverage ``more" information from both the spatial and the short-term temporal domains, but suffer from unreliable performance under complex conditions while incurring computational redundancy. In this paper, we explore the ``more essential" information from a more crucial domain for the detection. Through theoretical analysis, we reveal that the global temporal saliency and correlation information in the temporal profile demonstrate significant superiority in distinguishing target signals from other signals. To investigate whether such superiority is preferentially leveraged by well-trained networks, we built the first prediction attribution tool in this field and verified the importance of the temporal profile information. Inspired by the above conclusions, we remodel the IRST detection task as a one-dimensional signal anomaly detection task, and propose an efficient deep temporal probe network (DeepPro) that only performs calculations in the time dimension for IRST detection. We conducted extensive experiments to fully validate the effectiveness of our method. The experimental results are exciting, as our DeepPro outperforms existing state-of-the-art IRST detection methods on widely-used benchmarks with extremely high efficiency, and achieves a significant improvement on dim targets and in complex scenarios. We provide a new modeling domain, a new insight, a new method, and a new performance, which can promote the development of IRST detection. Codes are available at https://github.com/TinaLRJ/DeepPro.

翻译：红外小目标检测因目标信号极其微弱且背景干扰强烈，在同时实现精确、鲁棒和高效性能方面面临挑战。当前基于学习的方法试图从空间域和短期时域中利用"更多"信息，但在复杂条件下性能不可靠，同时存在计算冗余。本文从一个对检测更为关键的领域探索"更本质"的信息。通过理论分析，我们揭示了时序剖面中的全局时序显著性和相关性信息在区分目标信号与其他信号方面展现出显著优势。为探究训练良好的网络是否优先利用这种优势，我们构建了该领域首个预测归因工具，验证了时序剖面信息的重要性。受上述结论启发，我们将红外小目标检测任务重塑为一维信号异常检测任务，并提出一种高效深度时序探测网络（DeepPro），该网络仅在时间维度进行计算以实现红外小目标检测。我们进行了大量实验以充分验证方法的有效性。实验结果令人振奋：我们的DeepPro在广泛使用的基准数据集上以极高的效率超越了现有最先进的红外小目标检测方法，并在微弱目标检测和复杂场景中实现了显著提升。我们提供了一个新的建模领域、一种新视角、一种新方法和新的性能标杆，这将推动红外小目标检测领域的发展。代码发布于 https://github.com/TinaLRJ/DeepPro。