In infrared imaging, small targets often exhibit indistinct contours and sparse texture information, presenting a significant challenge for identification based solely on their inherent characteristics. To address this limitation, a novel mixed-frequency feature detection (MFFD) model is proposed. This model substantially improves small-object detection performance by leveraging both the contextual information of the target and its surrounding background. The MFFD model introduces a mixed-frequency extraction module that enhances small-target recognition by integrating global low-frequency semantic features with local high-frequency target details. Additionally, a multi-stage fusion module is employed to effectively coordinate feature interaction and integration across multiple levels, thereby improving semantic understanding and spatial information fusion. On the publicly available NUAA-SIRST and IRSTD-1k datasets, MFFD-Net outperformed five other deep learning-based methods. Compared to AGPC-Net, MFFD-Net achieved significant improvements in IoU and nIoU metrics. For the NUAA-SIRST dataset, increases of 4.42% and 4.33% were observed, respectively, while for the IRSTD-1k dataset, the corresponding improvements were 3.63% and 6.38%. These results demonstrate the strong potential of the proposed model for detecting small objects in complex infrared backgrounds.