Toward Dense Moving Infrared Small Target Detection: New Datasets and Baseline

As an important research branch of infrared small target detection, dense target detection (e.g., drone swarm detection) has always been a topic worth exploring. Currently, existing datasets cover only one or several (sparse) targets, with almost no dataset available for the research on dense small target detection. To advance this kind of search, for the first time, we synthesize two special dense moving target datasets (DMIST-60 and DMIST-100) on DAUB data. They both contain far more than 50 infrared small targets per frame. In the meantime, for evaluating our new datasets and flourishing detection methodology research, we propose a linking-aware sliced network (LASNet) as the baseline of our datasets. It mainly consists of visual feature extraction, motion feature extraction and motion-affinity fusion. The comprehensive experiments on our synthesized datasets confirm: i) both datasets are practical and effective for dense moving infrared small target detection and ii) proposed LASNet could always obviously outperform other compared methods in both sparse and dense target scenarios. Our new datasets and source codes are currently available at https://github.com/UESTC-nnLab/DMIST.