Based on YOLOV8, a specialized algorithm named Synthetic Aperture Radar(SAR)-You Only Look Once(YOLO) is developed for small target detection in SAR images. The proposed architecture incorporates a Pyramid Multi-Scale Feature Aggregation(PMSFA) module, an Efficient Multi-Scale Feature Fusion(FPSConv) module, and a Lightweight Shared Detection Convolutional Head(LSDECD) into the native YOLOV8 framework. The proposed algorithm is validated by using both a self-constructed SAR image small target detection dataset and publicly available datasets. Ablation studies demonstrate the effectiveness of the three introduced modules. Comparative experiments further indicate that the proposed SAR-YOLO algorithm significantly improves small target detection accuracy compared to other object detection methods: the key metric mAP50 is increased by 11.93%, while the detection time per frame is reduced to 18 ms. The proposed algorithm provides a robust, accurate, and real-time detection framework for small target detection in SAR images.