To address the existing PCB defect detection methods with high miss detection rate,poor generalisation and difficulty in balancing detection accuracy and speed, this paper proposes a PCB defect detection algorithm YOLOv8-CSM based on the improved YOLOv8n model. Firstly, a CoordAttention module is added at the end of the backbone network, which suppresses the influence of the complex background on the defective region of the PCB in order to improve the model′s detection accuracy; second, three SEAM modules are referenced in the detection header to expand the model receptive field and improve the model′s ability to identify small defects to reduce the miss detection rate; finally, MPDIoU is used to replace the traditional CIoU loss to optimise the regression effect of the bounding box and improve the convergence speed of the model. The experimental data show that YOLOv8-CSM can better balance detection accuracy and speed, and it is more generalizable. Compared with the base model, the Recall, Precision, mAP50 and FPS are improved by 4.3%, 1.8%, 2.7% and 42.76, respectively, which significantly enhances the model′s performance in PCB defect detection tasks.