Infrared small target detection refers to the segmentation of small targets from infrared images. This is of significance in the application of fire detection systems, maritime surveillance, and other rescue systems. However, because of factors such as small target size, inconspicuous features, and complex background environment, the detection performance of current infrared small target detection algorithms is generally limited. To address this issue, an infrared small target detection algorithm based on the Laplacian pyramid multi-level transformer (LPformer) was designed in this study. During network iteration, small infrared targets are prone to losing texture detail information owing to their small size. The Laplacian pyramid was used to extract different levels of high-frequency boundary information from the original input infrared image. A structural information conversion module was then fused with the features of different levels in the backbone network to compensate for the lost texture information. Next, to further improve the discriminative ability of the network and suppress the false alarm rate while improving the detection accuracy, a channel-based transformer structure that takes each channel feature map as tokens was also adopted. This calculated the self-attention map along the channel dimension. Experimental results demonstrated that the detection performance of the proposed algorithm was higher than that of current advanced detection algorithms.