HVUNet: hybrid vit-UNet for infrared dim and small target detection

Based on the Visual Transformer (ViT), the infrared dim and small target detection is a pioneering task in the field of deep learning. Existing ViT methods applied in the Unet global network utilize a single attention mechanism for each layer of the network, directing the network's focus towards the regions of dim and small targets in the images. However, these methods neglect the correlation between the encoding and decoding paths of the Unet network, failing to fully exploit the powerful feature extraction capabilities of ViT. Consequently, there is a continuous increase in the false negative and false positive rates of dim and small target detection.This paper proposes an improvement to the Unet-type network by introducing a novel multi-level ViT dim and small target detection network—HVUNet. Specifically, we design low-level feature extraction residual blocks to extract low-level features from each level of the image. Furthermore, we introduce three types of multi-head attention modules in the encoding, decoding, and concatenation paths respectively, to capture the long-range dependencies of the three paths. This overcomes the challenge of significant differences in the distribution of background and target information in infrared dim and small target images.Experimental results on public datasets demonstrate that our HVUNet significantly reduces the false negative and false positive rates of small target detection, thereby improving detection probability.