TMP: Temporal Motion Perception with spatial auxiliary enhancement for moving Infrared dim-small target detection

Abstract

Infrared dim and small target detection has significant research value and application prospects in both military affairs and civil safety scenes. This topic presents substantial challenges due to the small size of the targets and their low contrast against the background in images. In the past ten years, most detection methodologies have primarily focused on single-frame images, mainly relying on spatial-domain image features to identify dim and small targets, with limited consideration of temporal-domain motion features. However, unlike general objects, the image features of dim-small targets are often easy to be interfered by background, and even seriously lost in capturing stages. These deficiencies can be the primary factors to impact the detection performance of single-frame methods. To promote infrared dim-small target detection, this paper proposes a novel multi-frame pipeline called Temporal Motion Perception with spatial auxiliary enhancement (TMP), which consists of two parallel feature extraction branches. One auxiliary branch is designed to capture traditional image features from spatial domain, and the other is proposed to extract the motion features of targets from temporal domain. In addition, a new Complementary Symmetry Weighting module is specially designed to fulfill the cross-domain fusion of spatio-temporal features, further enhancing feature representation. Experimental results on three public datasets demonstrate the efficiency of our proposed scheme. Compared with existing methods, it could often refresh the state-of-the-art (SOTA) performance on most metrics. Our codes are available at https://github.com/UESTC-nnLab/TMP.