Neural Spatial-Temporal Tensor Representation for Infrared Small Target Detection

TL;DR

This paper introduces NeurSTT, a neural spatial-temporal tensor model that improves infrared small target detection by enhancing background modeling and reducing false positives in multi-frame sequences.

Contribution

The paper presents a novel neural-based framework for infrared small target detection that effectively models spatial-temporal features and simplifies optimization compared to traditional methods.

Findings

Outperforms existing methods in detection accuracy.

Uses 16.6 times fewer parameters.

Achieves 19.19% higher IoU on average.

Abstract

Optimization-based approaches dominate infrared small target detection as they leverage infrared imagery's intrinsic low-rankness and sparsity. While effective for single-frame images, they struggle with dynamic changes in multi-frame scenarios as traditional spatial-temporal representations often fail to adapt. To address these challenges, we introduce a Neural-represented Spatial-Temporal Tensor (NeurSTT) model. This framework employs nonlinear networks to enhance spatial-temporal feature correlations in background approximation, thereby supporting target detection in an unsupervised manner. Specifically, we employ neural layers to approximate sequential backgrounds within a low-rank informed deep scheme. A neural three-dimensional total variation is developed to refine background smoothness while reducing static target-like clusters in sequences. Traditional sparsity constraints are incorporated into the loss functions to preserve potential targets. By replacing complex solvers with a deep updating strategy, NeurSTT simplifies the optimization process in a domain-awareness way. Visual and numerical results across various datasets demonstrate that our method outperforms detection challenges. Notably, it has 16.6$\times$ fewer parameters and averaged 19.19\% higher in $IoU$ compared to the suboptimal method on $256 \times 256$ sequences.