Aiming at the problem of low detection accuracy caused by small target features in complex backgrounds, which are submerged by background noise after multiple convolutions, a one-stage lightweight small target detection algorithm SA-YOLO is proposed to enhance weak feature expression. First, the improved ShuffleNetv2 network is used to build the backbone network, and by embedding the SE attention module and the Inception structure, the feature extraction ability of the network in complex backgrounds is improved, background noise is effectively suppressed, and weak features are fully extracted. Second, in the neck network, a new feature fusion module is adopted to adjust the weights of high-level features based on the spatial location information of low-level feature blocks containing more weak features, so as to improve the utilization rate of feature fusion at different levels and reduce the feature loss of small targets. Finally, it replaces the original YOLO-coupled detection head with the decoupled detection head, decouples thei classification task and the regression task, improves the decoding ability of weak features, and enhances the performance of small target detection. Experiments are carried out on the public dataset COCO2017, and the results show that the parameter size of the SA-YOLO is only 1.14M, and the average detection recall rate $\rm AR_S$ of small targets reaches 31.6%. At the same time, the proposed algorithm is compared with the mainstream algorithms in recent years. The results show that the proposed algorithm has strong competitiveness in small target detection.