  Due to the complicated background and noise of infrared images, infrared
small target detection is one of the most difficult problems in the field of
computer vision. In most existing studies, semantic segmentation methods are
typically used to achieve better results. The centroid of each target is
calculated from the segmentation map as the detection result. In contrast, we
propose a novel end-to-end framework for infrared small target detection and
segmentation in this paper. First, with the use of UNet as the backbone to
maintain resolution and semantic information, our model can achieve a higher
detection accuracy than other state-of-the-art methods by attaching a simple
anchor-free head. Then, a pyramid pool module is used to further extract
features and improve the precision of target segmentation. Next, we use
semantic segmentation tasks that pay more attention to pixel-level features to
assist in the training process of object detection, which increases the average
precision and allows the model to detect some targets that were previously not
detectable. Furthermore, we develop a multi-task framework for infrared small
target detection and segmentation. Our multi-task learning model reduces
complexity by nearly half and speeds up inference by nearly twice compared to
the composite single-task model, while maintaining accuracy. The code and
models are publicly available at https://github.com/Chenastron/MTUNet.
