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

2025-06-15

AI Summary

Infrared small target detection remains challenging due to extremely low signal-to-noise ratios and strong background clutter, hindering simultaneous achievement of accuracy, robustness, and efficiency. To address this, we reformulate the task as one-dimensional time-series anomaly detection—abandoning conventional spatio-temporal fusion—and model only along the temporal dimension to eliminate spatial redundancy and enhance generalization. Our key contributions are threefold: (1) We identify global temporal saliency and correlation as fundamental discriminative cues for small targets; (2) We introduce the first prediction attribution analysis tool specifically designed for infrared small target detection; (3) We propose DeepPro, a lightweight, purely temporal-network architecture. Extensive experiments demonstrate that DeepPro achieves new state-of-the-art performance across major benchmarks, with significant gains on extremely dim targets and complex interference scenarios. Moreover, it accelerates inference by several-fold. The code is publicly available.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Time-Series/Data Streams
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs

Abstract

Infrared small target (IRST) detection is challenging in simultaneously achieving precise, universal, 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.

Problem

• Improving infrared small target detection accuracy and robustness
• Reducing computational redundancy in current detection methods
• Leveraging temporal profile for superior signal distinction

Innovation

• Leverages global temporal saliency and correlation
• Proposes deep temporal probe network (DeepPro)
• Remodels IRST detection as 1D anomaly detection