Bidirectional Temporal Information Propagation for Moving Infrared Small Target Detection

TL;DR

This paper introduces BIRD, a bidirectional temporal information propagation method that enhances moving infrared small target detection by leveraging both local and global temporal cues, resulting in improved accuracy and efficiency.

Contribution

The paper proposes a novel bidirectional propagation framework with local and global motion fusion modules, jointly optimized with detection and fusion losses, advancing infrared small target detection.

Findings

Achieves state-of-the-art detection performance.

Demonstrates faster inference speed.

Effectively models local and global temporal dependencies.

Abstract

Moving infrared small target detection is broadly adopted in infrared search and track systems, and has attracted considerable research focus in recent years. The existing learning-based multi-frame methods mainly aggregate the information of adjacent frames in a sliding window fashion to assist the detection of the current frame. However, the sliding-window-based methods do not consider joint optimization of the entire video clip and ignore the global temporal information outside the sliding window, resulting in redundant computation and sub-optimal performance. In this paper, we propose a Bidirectional temporal information propagation method for moving InfraRed small target Detection, dubbed BIRD. The bidirectional propagation strategy simultaneously utilizes local temporal information of adjacent frames and global temporal information of past and future frames in a recursive fashion. Specifically, in the forward and backward propagation branches, we first design a Local Temporal Motion Fusion (LTMF) module to model local spatio-temporal dependency between a target frame and its two adjacent frames. Then, a Global Temporal Motion Fusion (GTMF) module is developed to further aggregate the global propagation feature with the local fusion feature. Finally, the bidirectional aggregated features are fused and input into the detection head for detection. In addition, the entire video clip is jointly optimized by the traditional detection loss and the additional Spatio-Temporal Fusion (STF) loss. Extensive experiments demonstrate that the proposed BIRD method not only achieves the state-of-the-art performance but also shows a fast inference speed.