Consistent Learning of Sparse Background Features for Infrared Small-Target Labeling

Recent years have witnessed many remarkable achievements in infrared small target detection based on deep learning. To achieve high performance in real-world applications, deep learning methods require a substantial number of accurate labels. However, infrared small target annotating is labor intensive as they are very small. Determining and annotating edge pixels demands considerable time and effort, which slows down data expansion and further research in infrared small target detection. To mitigate the issue, we propose a pseudo-label generation method named Consistent Learning of Sparse Background Feature (CLSBF). This approach models the generation of infrared small target pseudo-labels as a domain transformation from local target maps to background ones. It can relax the supervision requirements of deep learning methods, transitioning from absolute pixel-level supervision to point supervision. This method employs an unsupervised approach primarily, supplemented by semi-simulated supervision, to achieve mutual conversion of local images from different domains and obtain the final target pseudo-labels through the differences between target images and transformed background ones. Experiments show that equipped with our method, models trained with the input of a coarsely accurate center label can achieve performance up to 99.94% compared to models trained with official accurate labels. Furthermore, when the official labels are not accurate enough, models trained with pseudo-labels generated by CLSBF consistently show a performance improvement of 1.11% to 4.09% when they are evaluated based on re-labeled bounding boxes. Extensive experiments demonstrate the reliability of CLSBF in generating pseudo-labels and its potential to alleviate the labor-intensive process of manual labeling significantly. We have released an infrared small target labeling tool with CLSBF as an assistant at https://github.com/SeaHifly/CLSBF_software.git.