Small-object detection presents a formidable challenge in object detection. While object detectors leveraging convolutional neural networks have shown remarkable advancements, the downsampling of images in current detectors results in the loss of spatial domain information. Addressing this issue, we propose SFDet, a small-object detection method that employs an attention mechanism shifting from the spatial to the frequency domain, specifically optimized for small-object detection in underwater images. Specifically, our approach incorporates a fusion mechanism that combines image enhancement networks for semantic enhancement and extracts a composite representation of spatial and frequency domain components to enhance small-object detection accuracy. We evaluate our proposed approach on four publicly available datasets, and the results demonstrate its superior performance compared with other methods. The code is available at: https://github.com/fadaishaitaiyang/SFDet.git