Small target detection is a challenging research task in the field of computer vision. For the problems of small target object size ,inconspicuous features and target aggregation ,a small target detection algorithm C-SODNET based on adaptive feature fusion and task alignment is proposed. The algorithm is optimized and improved on the basis of TOOD by introducing ConvNeXt as the backbone network ,improving the feature pyramid structure by embedding CBAM attention mechanism and adaptive feature fusion module feature extraction capability of the region of interest ,while adding deformable convolution in the detection head significantly improves the detection capability for small target objects ,and finally introducing CIoU regression loss function to train the model. The experimental results show that the mAP50 of C-SODNET in VisDrone2019 small target detection dataset is 51. 2% ,which improves the accuracy rate by 9. 4% compared to the TOOD algorithm ,and the accuracy rate APs of small target objects improves by 7. 3% ,which verifies the effectiveness of the algorithm. This algorithm can provide an effective solution for small target detection ap- plications in high-altitude or long-range scenes.