To address the challenges in small object detection tasks,such as the small size of target images,blurred target features,and difficulty in distinguishing targets from backgrounds,a method based on dual-stream contrastive feature learning and multi-scale image degradation augmentation is proposed.First,the input images of the contrastive learning model are subjected to multi-scale degradation augmentation,thus enhancing the model's ability to perceive and capture small targets.Second,contrastive learning representations are conducted in both spatial and frequency domains simultaneously to learn more discriminative target recognition features,thereby improving the model's ability to differentiate between targets and backgrounds.To verify the effectiveness of the proposed scheme,ablation experiments are designed,and the detection performance is compared with that of other advanced algorithms.Experimental results show that the proposed scheme achieves an improvement of 3.6％ in mean Average Precision (mAP) over the baseline algorithm on the MS COCO dataset,and an improvement of 7.7％ in mAP for small objects (mAP_S) compared to mainstream advanced algorithms.On the VisDrone2019 dataset,the proposed method achieves a 2.4％ increase in mAP compared to the baseline algorithm,demonstrating its superior overall performance over the baseline algorithm and other mainstream advanced algorithms.Visual analysis of detection results indicates a significant improvement in the rates of false negatives and false positives for small object detection.