TAPM-Net: Trajectory-Aware Perturbation Modeling for Infrared Small Target Detection

TL;DR

TAPM-Net introduces a novel trajectory-aware perturbation modeling approach for infrared small target detection, effectively capturing directional feature disturbances to improve detection accuracy in cluttered scenes.

Contribution

The paper proposes TAPM-Net with two new modules, PGM and TASB, to explicitly model target-induced feature trajectories, enhancing detection performance over existing CNN and ViT methods.

Findings

Achieves state-of-the-art results on NUAA-SIRST and IRSTD-1K datasets.

Effectively models directional feature propagation for small target detection.

Maintains low computational cost while improving detection accuracy.

Abstract

Infrared small target detection (ISTD) remains a long-standing challenge due to weak signal contrast, limited spatial extent, and cluttered backgrounds. Despite performance improvements from convolutional neural networks (CNNs) and Vision Transformers (ViTs), current models lack a mechanism to trace how small targets trigger directional, layer-wise perturbations in the feature space, which is an essential cue for distinguishing signal from structured noise in infrared scenes. To address this limitation, we propose the Trajectory-Aware Mamba Propagation Network (TAPM-Net), which explicitly models the spatial diffusion behavior of target-induced feature disturbances. TAPM-Net is built upon two novel components: a Perturbation-guided Path Module (PGM) and a Trajectory-Aware State Block (TASB). The PGM constructs perturbation energy fields from multi-level features and extracts gradient-following feature trajectories that reflect the directionality of local responses. The resulting feature trajectories are fed into the TASB, a Mamba-based state-space unit that models dynamic propagation along each trajectory while incorporating velocity-constrained diffusion and semantically aligned feature fusion from word-level and sentence-level embeddings. Unlike existing attention-based methods, TAPM-Net enables anisotropic, context-sensitive state transitions along spatial trajectories while maintaining global coherence at low computational cost. Experiments on NUAA-SIRST and IRSTD-1K demonstrate that TAPM-Net achieves state-of-the-art performance in ISTD.