Deformable Feature Alignment and Refinement for Moving Infrared Dim-small Target Detection

TL;DR

This paper introduces a deformable feature alignment and refinement method for detecting moving infrared small targets, explicitly utilizing motion information during both training and inference to improve detection accuracy.

Contribution

The paper proposes a novel DFAR method with a Temporal Deformable Alignment module and an Attention-guided Deformable Fusion block, explicitly incorporating motion compensation in infrared target detection.

Findings

Achieves state-of-the-art results on DAUB and IRDST datasets.

Effectively handles large motion in infrared small target detection.

Outperforms existing methods in accuracy and robustness.

Abstract

The detection of moving infrared dim-small targets has been a challenging and prevalent research topic. The current state-of-the-art methods are mainly based on ConvLSTM to aggregate information from adjacent frames to facilitate the detection of the current frame. However, these methods implicitly utilize motion information only in the training stage and fail to explicitly explore motion compensation, resulting in poor performance in the case of a video sequence including large motion. In this paper, we propose a Deformable Feature Alignment and Refinement (DFAR) method based on deformable convolution to explicitly use motion context in both the training and inference stages. Specifically, a Temporal Deformable Alignment (TDA) module based on the designed Dilated Convolution Attention Fusion (DCAF) block is developed to explicitly align the adjacent frames with the current frame at the feature level. Then, the feature refinement module adaptively fuses the aligned features and further aggregates useful spatio-temporal information by means of the proposed Attention-guided Deformable Fusion (AGDF) block. In addition, to improve the alignment of adjacent frames with the current frame, we extend the traditional loss function by introducing a new motion compensation loss. Extensive experimental results demonstrate that the proposed DFAR method achieves the state-of-the-art performance on two benchmark datasets including DAUB and IRDST.