Dynamic Background Reconstruction via MAE for Infrared Small Target Detection

TL;DR

This paper introduces a novel infrared small target detection method called Dynamic Background Reconstruction (DBR) that leverages Vision Transformers and dynamic shifting to effectively reconstruct backgrounds and improve detection accuracy in complex scenes.

Contribution

The paper proposes a new ISTD approach using Vision Transformers with a grid masking strategy and dynamic shifting, enhancing background reconstruction and detection performance.

Findings

Achieves the highest F1-score on MFIRST dataset (64.10%)

Achieves the highest F1-score on SIRST dataset (75.01%)

Effectively reduces false positives in small target detection

Abstract

Infrared small target detection (ISTD) under complex backgrounds is a difficult problem, for the differences between targets and backgrounds are not easy to distinguish. Background reconstruction is one of the methods to deal with this problem. This paper proposes an ISTD method based on background reconstruction called Dynamic Background Reconstruction (DBR). DBR consists of three modules: a dynamic shift window module (DSW), a background reconstruction module (BR), and a detection head (DH). BR takes advantage of Vision Transformers in reconstructing missing patches and adopts a grid masking strategy with a masking ratio of 50\% to reconstruct clean backgrounds without targets. To avoid dividing one target into two neighboring patches, resulting in reconstructing failure, DSW is performed before input embedding. DSW calculates offsets, according to which infrared images dynamically shift. To reduce False Positive (FP) cases caused by regarding reconstruction errors as targets, DH utilizes a structure of densely connected Transformer to further improve the detection performance. Experimental results show that DBR achieves the best F1-score on the two ISTD datasets, MFIRST (64.10\%) and SIRST (75.01\%).