Probing Deep into Temporal Profile Makes the Infrared Small Target Detector Much Better

TL;DR

This paper introduces DeepPro, a deep temporal probe network that models infrared small target detection as a one-dimensional signal anomaly detection task, significantly improving accuracy and efficiency especially for dim targets in complex scenarios.

Contribution

It reveals the importance of global temporal saliency in IRST detection and proposes a novel, efficient deep temporal model that outperforms existing methods.

Findings

DeepPro outperforms state-of-the-art IRST detection methods.

The method achieves high efficiency by focusing only on the temporal dimension.

DeepPro shows significant improvements in detecting dim targets in complex scenarios.

Abstract

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://tinalrj.github.io/DeepPro/.