In addressing challenges such as small target sizes, blurred target features, and difficulty distinguishing between targets and backgrounds in small object detection tasks, a method based on dual-stream contrastive feature learning and image multi-scale degradation augmentation is proposed. Firstly, the input images of the contrastive learning model are subjected to multi-scale degradation augmentation, enhancing the model's perception of capturing small targets. Secondly, contrastive learning representations are conducted in both spatial and frequency domains simultaneously to learn more discriminative target recognition features, improving the model's ability to differentiate between targets and backgrounds and thus enhancing small object detection. To validate the effectiveness of the proposed method, ablation experiments are designed, and the detection performance is compared with other advanced algorithms. Experimental results show that the proposed method achieves a 3.6% improvement in average precision (mAP) compared to the baseline algorithm on the MS COCO datasets, a 7.7% improvement in average precision for small objects (APS) compared to mainstream advanced algorithms. On the VisDrone2019 datasets, the proposed method achieves a 2.4% increase in mAP compared to the baseline algorithm, demonstrating comprehensive performance superiority over the baseline algorithm and other mainstream advanced algorithms. Visual analysis of detection results indicates significant improvements in false negatives and false positives for small object detection using the proposed method.