In view of the problem that the current PCB defect detection algorithm cannot simultaneously take into account the number of model parameters and detection accuracy, this paper proposes an improved lightweight PCB detection algorithm ST-YOLO based on YOLOv8n. First, the backbone network of YOLOv8n was replaced by the lightweight backbone network StarNet to adjust the network structure. Delete the large target detection layer and add the small target detection layer. Secondly, C2f module is combined with Star Block and CA attention mechanism to design C2f-Star-CA module, which can better integrate local and global context information. Finally, lightweight detection headers are designed to reduce the number of parameters in the model by using shared convolution. The experimental results show that compared with YOLOv8n, the number of model parameters is reduced by 45.5%, the calculation amount is reduced by 56.8%, and the mAP%0.5 is increased by 0.2%. It provides new possibilities to meet the needs of mobile deployment.