Infrared small target (IST) detection plays a critical role in both civilian and military applications. However, in infrared remote sensing images, targets typically appear small, lack detailed textural information, low energy, and resolution. Previous object detection methods encounter difficulties in accurately identifying targets and avoiding mistakenly identifying interference as targets when detecting IST in complex background environments. To address these issues, we propose STASPPNet, an IST detection network based on a Swin Transformer and multiscale atrous spatial pyramid pooling, which can detect ISTs in complex backgrounds. A Swin-convolution (Swin-Conv) backbone is designed to enhance feature extraction for ISTs. In addition, we create the multiscale atrous spatial pyramid pooling module (MASPPM), leveraging atrous convolutions and max pooling layers to improve feature representation, leading to more accurate detection. Moreover, we develop the multiscale feature detection head (MFDH), enabling the detection of targets in infrared images with as few as a dozen pixels and enhancing the performance for weak infrared targets. Extensive experiments illustrate that STASPPNet achieves an average accuracy of 96.9% for mAP50 and 49.7% for mAP50-95 on the NUAA-SIRST dataset and outperforms other state-of-the-art networks. The ablation experiments prove the effectiveness of the proposed module and network. The source code of the STASPPNet will be available at https://github.com/hwuscut/STASPPNet.