With the rapid development of modern electronic manufacturing industry, an improved lightweight YOLOv11 circuit board defect detection method was proposed in order to solve the problems of low precision and low detection efficiency of small target defect detection on the surface of printed circuit board. Firstly, a new lightweight network StarNet was used as the feature extraction network, which had significant computational complexity. At the same time, FasterNet was introduced to construct the C3k2_Faster module to achieve lightweight neck network, effective model calculation cost, and improve feature extraction capabilities and fusion speed. In addition, the loss function was replaced by Inner-SIoU, and a scale factor was introduced to control the auxiliary bounding boxes of different sizes to speed up the convergence of the model and improve the regression accuracy. Finally, in order to realize the lightweight application requirements of the algorithm in mobile and embedded scenarios, layer-adaptive magnitude-based pruning (LAMP) pruning was used to compress the model while ensuring the accuracy of the model, and the parameters and sizes of the model were further improved. The experimental results show that the weighted file size of the improved model is only 1.5 MB, which is 72.7% lower than that of the baseline model, while the calculation amount is 76.7% lower, and the accuracy is 3.4% higher. The proposed improved algorithm performs well in defect detection tasks and is easy to deploy in practical applications.